Analysis of the Broad’s Avana CRISPR (Broad Institute Cancer Dependency Map 2018, Meyers et al. 2017) and the Broad and Dana-Farber Cancer Institite’s Achilles shRNA (MacFarland et al. 2018, Data Science 2018).

The Avana screen produced results using CERES (Meyers et al. 2017) (GitHub), which generates gene dependency scores from sgRNA depletion scores from gene essentiality screens and eliminates bias arising from the effect of copy number variation on Cas9 DNA cleavage. The lower the CERES score, the higher the likelihood that the gene is essential in the associated cell line. Scores are scaled per cell line such that a score of 0 is the median effect of nonessential genes and -1 is the median effect of common core essential genes.

In a previous version of the shRNA screen, DEMETER (GitHub) was used to compute a dependency score for each gene by using the depletion values from each shRNA to infer the effect of target knockdown (on-target) and of expressing a given miRNA seed (off-target) in each cell line. More negative values indicate increased dependency, while more positive values indicate lower dependency. Zero represents the avergae dependency across all cell lines.

In the new data release, DEMETER2 (GitHub repository), developed by McFarland et al. (2018) was used to analyze the Achilles screen. DEMETER2 expands on DEMETER by including parameters for cell-line-specific screen effects and noisy data, correcting for global differences in shRNA levels across cell lines, pooling data acorss cell lines via hierarchical modeling, and using Bayesian inference to compute uncertainty estimates. Now, a score of zero represents no dependency.

All annotation files (copy number, mutation status, and gene expression) (Consortium and Consortium 2015, Barretina et al. 2012) were downloaded from the DepMap Data Portal.

1 Set up


Load libraries.

library(NMF)
library(ggpubr)
library(ggsignif)
library(rowr)
library(data.table)
library(CePa)
library(plyr)
library(tidyverse)
library(magrittr)
library(matrixStats)
library(parallel)
library(kableExtra)
library(gridExtra)
library(broom)
library(glmnet)
library(devtools)
library(reshape2)
library(caret)
library(bsselectR)
library(ComplexHeatmap)
library(circlize)

2 Functions


2.1 Significance thresholding

adj_signif <- function(df) {
  # Takes df from compare_means and creates a signif code column for adjusted p-vals
  df$p.signif <- ifelse(df$p.signif == "ns", NA, df$p.signif)
  df$p.signif.adj <- ifelse(df$p.adj <= 0.0001, "****",
                     ifelse(df$p.adj <= 0.001, "***",
                     ifelse(df$p.adj <= 0.01, "**",
                     ifelse(df$p.adj <= 0.05, "*", NA))))
  df$p.short <- formatC(df$p, format = "g", digits =  2)
  df$p.adj.short <- formatC(df$p.adj, format = "g", digits =  2)
  
  return(df)
}

2.2 Make CRISPR grobs

makeCRISPRgrob <- function(g) {
  
  gene <- filter(crispr_data, Hugo_Symbol == g)
  
  # Mutation status
  crispr_color <- as.character(gene$Color)
  names(crispr_color) <- gene$Mutation_Status
  sig <- filter(crispr_signif, Hugo_Symbol == g)
  plot_mut <- ggplot(data = gene, aes(x = Mutation_Status, y = Score, color = Mutation_Status)) +
    geom_boxplot(outlier.shape = NA) +
    geom_jitter(alpha = 0.3, size = 0.7, position = position_jitter(w = 0.05)) +
    scale_color_manual(values = crispr_color) +
    geom_hline(yintercept = 0, linetype = 2, lwd = 0.3) +
    theme_light() +
    theme(legend.position = "none") +
    labs(x = paste0(g, " Mutation Status"), y = "CERES Score",
         title = "Mutation Status",
         subtitle = paste0("Wilcoxon test:\n- BH-corrected p-value: ", sig$p.adj.short, "\n- Uncorrected p-value: ", sig$p.short, "\nMutant lines harbor deleterious mutations."))
  
  # Copy number
  lo_cn <- range(gene$Copy_Number[!is.na(gene$Copy_Number)])[1]
  hi_cn <- range(gene$Copy_Number[!is.na(gene$Copy_Number)])[2]
  mid_cn <- (hi_cn - lo_cn) / 2
  plot_cn <- ggplot(data = gene, aes(x = Copy_Number, y = Score, color = Mutation_Status)) +
    geom_point(size = 0.5, alpha = 0.5) +
    geom_smooth(method = "lm", size = 0.5) +
    geom_hline(yintercept = 0, linetype = 2, lwd = 0.3) +
    scale_color_manual(values = crispr_color) +
    stat_cor(method = "pearson", show.legend = FALSE, label.x = c((lo_cn + mid_cn) / 2, (hi_cn - mid_cn) / 2 + mid_cn), label.y = max(gene$Score)) +
    theme(legend.position = "none") +
    labs(y = "CERES Score", color = "Mutation Status",
         x = paste0(g, " Theoretical Copy number"), title = "Copy Number",
         subtitle = "r: Pearson correlation coeffcient")
  
  # Gene expression
  lo_ge <- range(gene$RPKM_log2[!is.na(gene$RPKM_log2)])[1]
  hi_ge <- range(gene$RPKM_log2[!is.na(gene$RPKM_log2)])[2]
  mid_ge <- (hi_ge - lo_ge) / 2
  plot_ge <- ggplot(data = gene, aes(x = RPKM_log2, y = Score, color = Mutation_Status)) +
    geom_point(size = 0.5, alpha = 0.5) +
    geom_smooth(method = "lm", size = 0.5) +
    geom_hline(yintercept = 0, linetype = 2, lwd = 0.3) +
    scale_color_manual(values = crispr_color) +
    stat_cor(method = "pearson", show.legend = FALSE, label.x = c((lo_ge + mid_ge) / 2, (hi_ge - mid_ge) / 2 + mid_ge), label.y = max(gene$Score)) +
    theme(legend.position = "none") +
    labs(y = "CERES Score", color = "Mutation Status",
         x = paste0(g, " Gene Expression [log2(RPKM)]"),
         title = "Gene Expression",
         subtitle = "r: Pearson correlation coeffcient")
  
  # Cell line lineage
  plot_tissue <- ggplot(data = gene, aes(x = primary_tissue, y = Score, color = Mutation_Status)) +
    geom_point(alpha = 0.5) +
    scale_color_manual(values = crispr_color) +
    geom_hline(yintercept = 0, linetype = 2, lwd = 0.3) +
    scale_y_continuous(sec.axis = sec_axis(~ .)) +
    coord_flip() +
    theme(legend.position = "none") +
    labs(y = "CERES Score", x = "Primary Tissue",
         color = "Mutation Status", title = g)
  
  # Arrange plots
  plot <- ggarrange(ggarrange(plot_tissue, nrow = 1, labels = c("A")),
                    ggarrange(plot_mut, plot_cn, plot_ge, nrow = 3,
                              labels = c("B", "C", "D"), heights = c(2, 3, 3)),
                    font.label = list(size = 30, face = "bold"),
                    nrow = 1, ncol = 2, widths = c(3, 2))
  return(plot)
}

2.3 Make lineage plot

makeCRISPRlinplot <- function(g) {
  sig <- filter(crispr_signif_lineage, Hugo_Symbol == g)
  if(nrow(sig) == 0) {
    return(NULL)
  }
  else {
    gene <- filter(crispr_data, Hugo_Symbol == g)
    gene <- merge(gene, sig, by = c("Hugo_Symbol", "group_general_lineage_name"))
    gene <- mutate(gene, group_general_lineage_name = reorder(group_general_lineage_name, p, mean))
    crispr_color <- as.character(gene$Color)
    names(crispr_color) <- gene$Mutation_Status
    score_range <- abs(range(gene$Score)[2] - range(gene$Score)[1])
    round_accuracy <- ifelse(score_range <= 2, 0.25,
                             ifelse(score_range <= 3, 0.5, 1.0))
    
    plot <- ggplot(data = gene, aes(x = group_general_lineage_name, y = Score)) +
      geom_point(alpha = 0.5, mapping = aes(color = Mutation_Status)) +
      scale_color_manual(values = crispr_color) +
      coord_cartesian(y = c(min(gene$Score), round_any(x = max(gene$Score), accuracy = round_accuracy, f = ceiling))) +
      geom_signif(data = gene, mapping = aes(xmin = group_general_lineage_name, xmax = group_general_lineage_name, annotations = paste(p.short, "\n", p.adj.short), y_position = max(gene$Score)), manual = TRUE, tip_length   = 0, size = 0, textsize = 3) +
      theme(legend.position = "top", axis.text.x = element_text(angle = 70, hjust = 1, size = 10)) +
      labs(y = "CERES Score",
           x = "Lineage",
           color = "Mutation Status",
           title = paste0(g, ": CERES score by cell line lineage"),
           subtitle = "Lineages sorted by increasing p-value; labels indicate unadjusted p-value / BH-corrected p-value.\nMutant lines harbor deleterious mutations.")
    return(plot)
  }
}

2.4 Make tissue plot

makeCRISPRtissueplot <- function(g) {
  sig <- filter(crispr_signif_tissue, Hugo_Symbol == g)
  if(nrow(sig) == 0) {
    return(NULL)
  }
  else {
    gene <- filter(crispr_data, Hugo_Symbol == g)
    gene <- merge(gene, sig, by = c("Hugo_Symbol", "primary_tissue"))
    gene <- mutate(gene, primary_tissue = reorder(primary_tissue, p, mean))
    crispr_color <- as.character(gene$Color)
    names(crispr_color) <- gene$Mutation_Status
    score_range <- abs(range(gene$Score)[2] - range(gene$Score)[1])
    round_accuracy <- ifelse(score_range <= 2, 0.25,
                             ifelse(score_range <= 3, 0.5, 1.0))
    
    plot <- ggplot(data = gene, aes(x = primary_tissue, y = Score)) +
      geom_point(alpha = 0.5, mapping = aes(color = Mutation_Status)) +
      scale_color_manual(values = crispr_color) +
      coord_cartesian(y = c(min(gene$Score), round_any(x = max(gene$Score), accuracy = round_accuracy, f = ceiling))) +
      geom_signif(data = gene, mapping = aes(xmin = primary_tissue, xmax = primary_tissue, annotations = paste(p.short, "\n", p.adj.short), y_position = max(gene$Score)), manual = TRUE, tip_length   = 0, size = 0, textsize = 3) +
      theme(legend.position = "top", axis.text.x = element_text(angle = 70, hjust = 1, size = 10)) +
      labs(y = "CERES Score",
           x = "Primary Tissue",
           color = "Mutation Status",
           title = paste0(g, ": CERES score by cell line primary tissue"),
           subtitle = "Primary tissues sorted by increasing p-value; labels indicate unadjusted p-value / BH-corrected p-value.\nMutant lines harbor deleterious mutations.")
    return(plot)
  }
}

3 Data management


3.1 D. Charytonowicz cell line converter

This comprehensive cancer cell line information curated by Daniel Charytonowicz.

ccl_converter <- read.delim("./data_munging/cell_line_database_v1_20180911.tsv", row.names = 1, sep = "\t", header = TRUE)

3.2 DepMap cell line metadata

ccl_info <- read.delim("./data_munging/DepMap-2018q3-celllines.csv", sep = ",", header = TRUE, na.strings = c("", NA))
ccl_info$Primary.Disease <- gsub("\\\\", "", ccl_info$Primary.Disease)
ccl_info$Primary.Disease <- gsub("Ewings", "Ewing's", ccl_info$Primary.Disease)

# From figshare
crispr_meta <- read.delim("./data_munging/sample_info_18Q3_crispr.csv", sep = ",", header = TRUE, na.strings = c("", NA))
colnames(crispr_meta)[7] <- "CCLE_Name"

3.3 CGC gene list

Select genes from the Cancer Gene Census (CGC). The list was pulled from the International Cancer Genome Consortium (ICGC) data portal (Advaced Search > Genes > Curated Gene Set > Cancer Gene Census).

cgc <- data_frame("Hugo_Symbol" = read.delim("./data_munging/gene-ids-for-set-Cancer Gene Census.tsv", header = FALSE, sep = "\t")[, 2])

3.4 CCLE MAF file

For mutation calling, get paired gene name and cell line fields in a data frame. For this analysis, we don’t care about how many mutations there are per gene or what type of mutations there are, so I didn’t save more information. I took unique gene-cell line combinations since we only cared about mutation presence/absence. Add a Mutation_Status column denoting all entries in MAF files as mutations present in the associated cell lines.

maf_raw <- read.delim("./data_munging/CCLE_DepMap_18q3_maf_20180718.txt.gz", header = TRUE, sep = "\t")
# Filter for cell lines in CRISPR screen
maf_raw <- filter(maf_raw, Broad_ID %in% unique(crispr_meta$Broad_ID))
colnames(maf_raw)[colnames(maf_raw) == "Tumor_Sample_Barcode"] <- "CCLE_Name"

# Select columns
maf_df <- subset(maf_raw, select = c("Hugo_Symbol", "CCLE_Name", "Broad_ID", "Variant_Classification", "isDeleterious", "Reference_Allele"))
maf_df$Var_Length <- nchar(as.character(maf_df$Reference_Allele))

# Add Mutation_Status column
maf_df$Mutation_Status_Deleterious <- ifelse(maf_df$isDeleterious == TRUE, "Mutant", "Other")
maf_df$Mutation_Status_Nonsilent <- ifelse(maf_df$Variant_Classification == "Silent", "Other", "Mutant")
maf_df$Mutation_Status_DeleteriousMissense <- ifelse(maf_df$isDeleterious == TRUE | maf_df$Variant_Classification == "Missense_Mutation", "Mutant", "Other")
maf_df <- unique(subset(maf_df, select = c("Hugo_Symbol", "CCLE_Name", "Broad_ID", "Mutation_Status_Deleterious", "Mutation_Status_Nonsilent", "Mutation_Status_DeleteriousMissense", "Var_Length")))

# Whole MAF summary table
maf_summ <- maf_raw[, c("Variant_Classification", "isDeleterious")] %>% group_by(Variant_Classification, isDeleterious) %>% tally()
maf_summ$Percent <- format(round(maf_summ$n / sum(maf_summ$n) * 100, 4), nsmall = 2)
maf_summ$isDeleterious <- ifelse(maf_summ$isDeleterious == "TRUE", "Yes", "No")

knitr::kable(maf_summ, caption = "Distribution of variant classifications in the MAF file") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive"))
Distribution of variant classifications in the MAF file
Variant_Classification isDeleterious n Percent
3’UTR No 1 0.0003
3’UTR Yes 30 0.0084
5’Flank No 8 0.0022
5’Flank Yes 34 0.0095
5’UTR No 2 0.0006
5’UTR Yes 15 0.0042
De_novo_Start_OutOfFrame Yes 2006 0.5595
Frame_Shift_Del Yes 12441 3.4698
Frame_Shift_Ins Yes 9215 2.5701
IGR No 6 0.0017
IGR Yes 44 0.0123
In_Frame_Del No 1747 0.4872
In_Frame_Ins No 442 0.1233
Intron No 9 0.0025
Intron Yes 187 0.0522
Missense_Mutation No 211359 58.9484
Nonsense_Mutation Yes 12711 3.5451
Nonstop_Mutation Yes 335 0.0934
Silent No 94225 26.2795
Splice_Site Yes 13180 3.6759
Start_Codon_Del Yes 25 0.0070
Start_Codon_Ins Yes 33 0.0092
Start_Codon_SNP No 402 0.1121
Stop_Codon_Del No 1 0.0003
Stop_Codon_Del Yes 52 0.0145
Stop_Codon_Ins Yes 39 0.0109
max(nchar(as.character(maf_raw$Reference_Allele)))
maf_raw %>% group_by(Variant_Type, isTCGAhotspot) %>% tally()

test <- filter(maf_raw, Hugo_Symbol == "KRAS")
test %>% group_by(isDeleterious) %>% tally()

3.5 CCLE copy number file

cn <- read.delim("./data_munging/public_18Q3_gene_cn.csv.gz", sep = ",", check.names = FALSE, header = TRUE)

# Convert log2 ratios (log2(CN/2)) to CN
cn[2:ncol(cn)] <- lapply(cn[2:ncol(cn)], function(x) 2 * (2 ^ x))

# Remove Entrez gene IDs from colnames
colnames(cn) <- gsub(" .*", "", colnames(cn))
colnames(cn)[1] <- "Broad_ID"

# Melt
cn_melt <- melt(data = cn, id.vars = "Broad_ID", measure.vars = colnames(cn[2:ncol(cn)]), variable.name = "Hugo_Symbol", value.name = "Copy_Number")

saveRDS(cn_melt, "./data_munging/rds/cn_melt_18Q3.rds", compress = "xz")
cn_melt <- readRDS("./data_munging/rds/cn_melt_18Q3.rds")

3.6 CCLE gene expression file

Gene expression data (Reads Per Kilobase of transcript, per Million mapped reads, RPKM).

ge <- read.delim("./../crispr_lineages_giant_files/CCLE_DepMap_18q3_RNAseq_RPKM_20180718.gct.gz", skip = 2, header = TRUE, sep = "\t", check.names = FALSE)

# Edit columns
ge$Name <- NULL
colnames(ge)[1] <- "Hugo_Symbol"

# Melt
ge_melt <- melt(data = ge, id.vars = "Hugo_Symbol", measure.vars = colnames(ge[2:ncol(ge)]), value.name = "RPKM")
## Split variable column
ge_melt <- with(ge_melt, cbind(Hugo_Symbol, colsplit(variable, pattern = " ", names = c("CCLE_Name", "Broad_ID")), RPKM))
## Remove parentheses around Broad IDs
ge_melt$Broad_ID <- gsub("\\(|\\)", "", ge_melt$Broad_ID)

saveRDS(ge_melt, "./../crispr_lineages_giant_files/ge_melt_18Q3.rds", compress = "xz")
ge_melt <- readRDS("./../crispr_lineages_giant_files/ge_melt_18Q3.rds")

3.7 DepMap dependency probabilities

dep <- read.delim("./data_munging/gene_dependency_18Q3.csv.gz", sep = ",", header  = TRUE, check.names = FALSE)
# Remove Entrez gene IDs from colnames
colnames(dep) <- gsub(" .*", "", colnames(dep))

3.8 Merge CRISPR data and annotations

The latest CRISPR CERES score data (18Q3, August 2018) was pulled from the DepMap Data Portal (Broad Institute Cancer Dependency Map 2018, Meyers et al. 2017).

crispr <- read.delim("./data_munging/gene_effect_18Q3.csv.gz", sep = ",", header  = TRUE, check.names = FALSE)
# Remove Entrez gene IDs from colnames
colnames(crispr) <- gsub(" .*", "", colnames(crispr))

Merge annotation data:

# Melt CRISPR dataset for merging
crispr_melt <- melt(crispr, id.vars = "Broad_ID", measure.vars = colnames(crispr)[2:ncol(crispr)], variable.name = "Hugo_Symbol", value.name = "Score")

# Melt dependency probabilities dataset for merging
dep_melt <- melt(dep, id.vars = "Broad_ID", measure.vars = colnames(dep)[2:ncol(dep)], variable.name = "Hugo_Symbol", value.name = "Dep_Prob")

# Merge dependency probabilities
crispr_melt <- merge(crispr_melt, dep_melt, by = c("Broad_ID", "Hugo_Symbol"), all.x = TRUE)

# Merge cell line metadata
crispr_melt <- merge(crispr_melt, ccl_info, by = "Broad_ID", all.x = TRUE)
crispr_melt <- merge(crispr_melt, crispr_meta, by = c("CCLE_Name", "Broad_ID"), all.x = TRUE)

# Merge mutation annotations
crispr_muts <- merge(crispr_melt, maf_df, by = c("Hugo_Symbol", "CCLE_Name", "Broad_ID"), all.x = TRUE)
crispr_muts$Hugo_Symbol <- factor(crispr_muts$Hugo_Symbol)
crispr_muts <- crispr_muts %>% mutate(Mutation_Status_Deleterious = if_else(is.na(Mutation_Status_Deleterious), "Other", Mutation_Status_Deleterious), Mutation_Status_DeleteriousMissense = if_else(is.na(Mutation_Status_DeleteriousMissense), "Other", Mutation_Status_DeleteriousMissense), Mutation_Status_Nonsilent = if_else(is.na(Mutation_Status_Nonsilent), "Other", Mutation_Status_Nonsilent))

# Summarize number of mutant and Other cell lines
crispr_muts_summ <- crispr_muts %>% group_by(Hugo_Symbol) %>%
  summarize(N_Deleterious_Other = sum(Mutation_Status_Deleterious == "Other"),
            N_Deleterious_Mutant = sum(Mutation_Status_Deleterious == "Mutant"),
            N_DeleteriousMissense_Other = sum(Mutation_Status_DeleteriousMissense == "Other"),
            N_DeleteriousMissense_Mutant = sum(Mutation_Status_DeleteriousMissense == "Mutant"),
            N_Nonsilent_Other = sum(Mutation_Status_Nonsilent == "Other"),
            N_Nonsilent_Mutant = sum(Mutation_Status_Nonsilent == "Mutant"))

# Merge test results back into full dataset, which restores information lost in the summarization
crispr_data <- merge(crispr_muts_summ, crispr_muts, by = "Hugo_Symbol")

# Add Color columns
crispr_data$Color_Deleterious <- ifelse(crispr_data$Mutation_Status_Deleterious == "Other", "cyan3", "darkorchid")
crispr_data$Color_Deleterious <- factor(crispr_data$Color_Deleterious)
crispr_data$Color_DeleteriousMissense <- ifelse(crispr_data$Mutation_Status_DeleteriousMissense == "Other", "cyan3", "darkorchid")
crispr_data$Color_DeleteriousMissense <- factor(crispr_data$Color_DeleteriousMissense)
crispr_data$Color_Nonsilent <- ifelse(crispr_data$Mutation_Status_Nonsilent == "Other", "cyan3", "darkorchid")
crispr_data$Color_Nonsilent <- factor(crispr_data$Color_Nonsilent)

# Cell line lineages
crispr_data <- merge(crispr_data, ccl_converter, by = c("CCLE_Name", "Broad_ID"), all.x = TRUE)
levels(crispr_data$lineage_name) <- sort(levels(crispr_data$lineage_name), decreasing = TRUE)

# Copy number
crispr_data <- merge(crispr_data, cn_melt, by = c("Hugo_Symbol", "Broad_ID"), all.x = TRUE)

# Gene expression (RPKM)
ge_filt <- filter(ge_melt, Hugo_Symbol %in% unique(crispr_data$Hugo_Symbol))
crispr_data <- merge(crispr_data, ge_filt, by = c("Hugo_Symbol", "Broad_ID", "CCLE_Name"), all.x = TRUE)
crispr_data$RPKM_log2 <- log2(crispr_data$RPKM + 0.0001)

saveRDS(crispr_data, "./../crispr_lineages_giant_files/crispr_data_18Q3.rds", compress = "xz")
crispr_data <- readRDS("./../crispr_lineages_giant_files/crispr_data_18Q3.rds")
crispr_ccl <- data.frame("Broad_ID" = crispr_data$Broad_ID)
# write.table(crispr_data, file = "~/Desktop/crispr_data.tsv", quote = FALSE, sep = "\t")

4 Wilcoxon tests: Point mutations


Filter for point mutations:

crispr_data_ptmuts <- filter(crispr_data, Var_Length == 1 | is.na(Var_Length))
test_ptmuts <- filter(crispr_data_ptmuts, is.na(group_general_lineage_name))
test_ptmuts <- filter(crispr_data_ptmuts, Hugo_Symbol == "KRAS")

4.1 Grouped by gene

4.1.1 Deleterious vs. Other

crispr_signif_del <- compare_means(Score ~ Mutation_Status_Deleterious, group.by = c("Hugo_Symbol"), data = crispr_data_ptmuts, method = "wilcox.test", p.adjust.method = "BH")
crispr_signif_del <- adj_signif(crispr_signif_del)
crispr_signif_del <- crispr_signif_del[order(crispr_signif_del$p),]
saveRDS(crispr_signif_del, "./data_munging/rds/crispr_signif_ptmuts_deleterious_gene.rds")

# write.table(crispr_signif_del, file = "~/Desktop/crispr_signif_ptmuts_deleterious_gene.csv", quote = FALSE, sep = ",", row.names = FALSE)
crispr_signif_del <- readRDS("./data_munging/rds/crispr_signif_ptmuts_deleterious_gene.rds")

knitr::kable(filter(crispr_signif_del, p < 0.01)[, c("Hugo_Symbol", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "Wilcoxon test results comparing deleterious mutant vs other cell lines, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
Wilcoxon test results comparing deleterious mutant vs other cell lines, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Hugo_Symbol p p.adj p.format p.signif p.signif.adj
PTEN 0.0000000 0.0000000 1.5e-12 **** ****
TP53 0.0000008 0.0050305 7.7e-07 **** **
ARID1A 0.0000061 0.0265330 6.1e-06 ****
UTP20 0.0000785 0.2560978 7.8e-05 **** NA
VHL 0.0001077 0.2812367 0.00011 *** NA
ARID1B 0.0003780 0.7461832 0.00038 *** NA
ZC3H13 0.0009610 0.7461832 0.00096 *** NA
ABCD4 0.0012457 0.7461832 0.00125 ** NA
RB1 0.0016928 0.7461832 0.00169 ** NA
RNF208 0.0019385 0.7461832 0.00194 ** NA
DENND1A 0.0020308 0.7461832 0.00203 ** NA
AJAP1 0.0021075 0.7461832 0.00211 ** NA
HAX1 0.0022315 0.7461832 0.00223 ** NA
NIPBL 0.0022374 0.7461832 0.00224 ** NA
TIMD4 0.0022527 0.7461832 0.00225 ** NA
ULK1 0.0023004 0.7461832 0.00230 ** NA
PIGR 0.0028348 0.7461832 0.00283 ** NA
TP53BP2 0.0029604 0.7461832 0.00296 ** NA
KLHL17 0.0038240 0.7461832 0.00382 ** NA
TUBGCP5 0.0039141 0.7461832 0.00391 ** NA
FMN1 0.0039763 0.7461832 0.00398 ** NA
AKAP13 0.0040426 0.7461832 0.00404 ** NA
PIK3R1 0.0040474 0.7461832 0.00405 ** NA
UGT2A3 0.0041569 0.7461832 0.00416 ** NA
ATAD5 0.0045229 0.7461832 0.00452 ** NA
TCERG1 0.0045495 0.7461832 0.00455 ** NA
KRT19 0.0046981 0.7461832 0.00470 ** NA
PPIL4 0.0047926 0.7461832 0.00479 ** NA
CR1 0.0048029 0.7461832 0.00480 ** NA
KIAA1731 0.0048110 0.7461832 0.00481 ** NA
RINT1 0.0048906 0.7461832 0.00489 ** NA
SIN3A 0.0049114 0.7461832 0.00491 ** NA
DLD 0.0053771 0.7461832 0.00538 ** NA
NPSR1 0.0054137 0.7461832 0.00541 ** NA
PTPRR 0.0055220 0.7461832 0.00552 ** NA
CSTF3 0.0055891 0.7461832 0.00559 ** NA
GSTM5 0.0058384 0.7461832 0.00584 ** NA
OLFML2B 0.0058575 0.7461832 0.00586 ** NA
ARID5B 0.0059256 0.7461832 0.00593 ** NA
TDRD7 0.0061289 0.7461832 0.00613 ** NA
NOC4L 0.0061581 0.7461832 0.00616 ** NA
KIAA1107 0.0061991 0.7461832 0.00620 ** NA
SLC28A2 0.0062191 0.7461832 0.00622 ** NA
PLXNA1 0.0062851 0.7461832 0.00629 ** NA
PLOD3 0.0062880 0.7461832 0.00629 ** NA
ZNF439 0.0063457 0.7461832 0.00635 ** NA
DMKN 0.0063484 0.7461832 0.00635 ** NA
EDNRB 0.0063517 0.7461832 0.00635 ** NA
UTP3 0.0065359 0.7461832 0.00654 ** NA
MSH6 0.0066097 0.7461832 0.00661 ** NA
ELTD1 0.0066592 0.7461832 0.00666 ** NA
ST7 0.0068047 0.7461832 0.00680 ** NA
IQCH 0.0068191 0.7461832 0.00682 ** NA
LILRB2 0.0071039 0.7461832 0.00710 ** NA
BRD4 0.0072117 0.7461832 0.00721 ** NA
UTS2B 0.0072533 0.7461832 0.00725 ** NA
KLHL1 0.0073975 0.7461832 0.00740 ** NA
RAD50 0.0075005 0.7461832 0.00750 ** NA
AMER2 0.0076915 0.7461832 0.00769 ** NA
DMWD 0.0077231 0.7461832 0.00772 ** NA
ATG2A 0.0077283 0.7461832 0.00773 ** NA
NPAT 0.0080167 0.7461832 0.00802 ** NA
SMARCB1 0.0081997 0.7461832 0.00820 ** NA
PAQR3 0.0082276 0.7461832 0.00823 ** NA
SF3B2 0.0084940 0.7461832 0.00849 ** NA
NGRN 0.0085338 0.7461832 0.00853 ** NA
SKOR1 0.0085338 0.7461832 0.00853 ** NA
PHLPP1 0.0085991 0.7461832 0.00860 ** NA
ZBTB25 0.0086381 0.7461832 0.00864 ** NA
ZNF124 0.0089578 0.7461832 0.00896 ** NA
TOP1MT 0.0089680 0.7461832 0.00897 ** NA
THEMIS 0.0092069 0.7461832 0.00921 ** NA
ESYT2 0.0092528 0.7461832 0.00925 ** NA
SRPR 0.0092882 0.7461832 0.00929 ** NA
PAX5 0.0093106 0.7461832 0.00931 ** NA
TTC22 0.0094474 0.7461832 0.00945 ** NA
BUB1 0.0095144 0.7461832 0.00951 ** NA
RIMBP3 0.0096462 0.7461832 0.00965 ** NA
wilcox_gene_plot <- ggplot(data = crispr_signif_del) +
  geom_histogram(aes(x = p, fill = "chartreuse4"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  geom_histogram(aes(x = p.adj, fill = "darkslategray3"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  scale_x_continuous(breaks = seq(0, 1, by = 0.05), labels = seq(0, 1, by = 0.05)) +
  scale_fill_manual(name = "P-values", values = c("chartreuse4" = "chartreuse4", "darkslategray3" = "darkslategray3"), labels = c("Unadjusted", "BH-adjusted")) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = c(0.1, 0.85)) +
  labs(x = "BH-adjusted p-values", y = "Frequency")
wilcox_gene_plot

4.1.2 Non-silent mutant vs. Other

crispr_signif_nonsilent <- compare_means(Score ~ Mutation_Status_Nonsilent, group.by = c("Hugo_Symbol"), data = crispr_data_ptmuts, method = "wilcox.test", p.adjust.method = "BH")
crispr_signif_nonsilent <- adj_signif(crispr_signif_nonsilent)
crispr_signif_nonsilent <- crispr_signif_nonsilent[order(crispr_signif_nonsilent$p),]
saveRDS(crispr_signif_nonsilent, "./data_munging/rds/crispr_signif_ptmuts_nonsilent_gene.rds")

# write.table(crispr_signif_nonsilent, file = "~/Desktop/crispr_signif_ptmuts_nonsilent_gene.csv", quote = FALSE, sep = ",", row.names = FALSE)
crispr_signif_nonsilent <- readRDS("./data_munging/rds/crispr_signif_ptmuts_nonsilent_gene.rds")

knitr::kable(filter(crispr_signif_nonsilent, p < 0.01)[, c("Hugo_Symbol", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "Wilcoxon test results comparing non-silent mutant vs other cell lines, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
Wilcoxon test results comparing non-silent mutant vs other cell lines, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Hugo_Symbol p p.adj p.format p.signif p.signif.adj
TP53 0.0000000 0.0000000 < 2e-16 **** ****
KRAS 0.0000000 0.0000000 < 2e-16 **** ****
NRAS 0.0000000 0.0000000 < 2e-16 **** ****
BRAF 0.0000000 0.0000000 < 2e-16 **** ****
PTEN 0.0000000 0.0000000 2.1e-15 **** ****
PIK3CA 0.0000000 0.0000000 9.6e-14 **** ****
CTNNB1 0.0000058 0.0143321 5.8e-06 ****
TCERG1 0.0000135 0.0290351 1.4e-05 ****
ARID1A 0.0000332 0.0633052 3.3e-05 **** NA
FCGBP 0.0000696 0.1195154 7.0e-05 **** NA
TPR 0.0000827 0.1213965 8.3e-05 **** NA
PIK3R1 0.0000848 0.1213965 8.5e-05 **** NA
TAOK2 0.0002289 0.3025013 0.00023 *** NA
SLC22A9 0.0002503 0.3032257 0.00025 *** NA
GSTM5 0.0002648 0.3032257 0.00026 *** NA
CPSF1 0.0003055 0.3280113 0.00031 *** NA
NIPBL 0.0003521 0.3306423 0.00035 *** NA
PIGW 0.0003638 0.3306423 0.00036 *** NA
C14orf39 0.0003657 0.3306423 0.00037 *** NA
TLX2 0.0004787 0.4111424 0.00048 *** NA
ARFGAP1 0.0005157 0.4156420 0.00052 *** NA
ZNF808 0.0005323 0.4156420 0.00053 *** NA
CD320 0.0005816 0.4343665 0.00058 *** NA
MAT2B 0.0007184 0.5086287 0.00072 *** NA
ZNF177 0.0007543 0.5086287 0.00075 *** NA
SYNPO2L 0.0007699 0.5086287 0.00077 *** NA
HRAS 0.0008408 0.5244580 0.00084 *** NA
UTP3 0.0008549 0.5244580 0.00085 *** NA
RNF208 0.0008907 0.5275698 0.00089 *** NA
KCNIP4 0.0009472 0.5423384 0.00095 *** NA
GOLGA3 0.0010340 0.5457674 0.00103 ** NA
DSEL 0.0010395 0.5457674 0.00104 ** NA
PROM1 0.0010485 0.5457674 0.00105 ** NA
PMPCA 0.0010990 0.5552227 0.00110 ** NA
OR52E8 0.0012095 0.5724468 0.00121 ** NA
OR13C4 0.0012702 0.5724468 0.00127 ** NA
TELO2 0.0012922 0.5724468 0.00129 ** NA
TAS1R2 0.0013163 0.5724468 0.00132 ** NA
ZNF439 0.0013227 0.5724468 0.00132 ** NA
MDN1 0.0013858 0.5724468 0.00139 ** NA
MTMR2 0.0014414 0.5724468 0.00144 ** NA
E2F1 0.0014486 0.5724468 0.00145 ** NA
LPO 0.0014636 0.5724468 0.00146 ** NA
ING2 0.0014811 0.5724468 0.00148 ** NA
PLA2G4F 0.0014997 0.5724468 0.00150 ** NA
MEF2C 0.0015423 0.5738410 0.00154 ** NA
UTP20 0.0015804 0.5738410 0.00158 ** NA
IQCH 0.0016077 0.5738410 0.00161 ** NA
PCDHB15 0.0016370 0.5738410 0.00164 ** NA
ACSM2B 0.0017107 0.5804214 0.00171 ** NA
OR8D1 0.0017233 0.5804214 0.00172 ** NA
RFC1 0.0018888 0.6183553 0.00189 ** NA
KIAA1211L 0.0019402 0.6183553 0.00194 ** NA
SENP8 0.0019559 0.6183553 0.00196 ** NA
RUVBL1 0.0019950 0.6183553 0.00200 ** NA
CLEC4C 0.0020667 0.6183553 0.00207 ** NA
PCDHA8 0.0020705 0.6183553 0.00207 ** NA
PI4K2A 0.0020961 0.6183553 0.00210 ** NA
KPNA6 0.0021334 0.6183553 0.00213 ** NA
POU2F1 0.0022102 0.6183553 0.00221 ** NA
BTBD11 0.0022318 0.6183553 0.00223 ** NA
FBXW7 0.0023109 0.6183553 0.00231 ** NA
ANKRD31 0.0023475 0.6183553 0.00235 ** NA
OR51T1 0.0023614 0.6183553 0.00236 ** NA
METTL17 0.0023923 0.6183553 0.00239 ** NA
CIITA 0.0024057 0.6183553 0.00241 ** NA
ADAM15 0.0024806 0.6183553 0.00248 ** NA
TUBB2A 0.0025373 0.6183553 0.00254 ** NA
RAB43 0.0025433 0.6183553 0.00254 ** NA
TAAR1 0.0025653 0.6183553 0.00257 ** NA
EZH2 0.0025733 0.6183553 0.00257 ** NA
ZDHHC24 0.0025919 0.6183553 0.00259 ** NA
SURF6 0.0027361 0.6245468 0.00274 ** NA
KIF11 0.0027512 0.6245468 0.00275 ** NA
VHL 0.0027587 0.6245468 0.00276 ** NA
GRM7 0.0027641 0.6245468 0.00276 ** NA
AVEN 0.0027997 0.6245468 0.00280 ** NA
OLFML2B 0.0029959 0.6248726 0.00300 ** NA
SAMM50 0.0030102 0.6248726 0.00301 ** NA
ACTA1 0.0030465 0.6248726 0.00305 ** NA
FRMD4B 0.0030803 0.6248726 0.00308 ** NA
SLC26A2 0.0030940 0.6248726 0.00309 ** NA
MYH13 0.0032183 0.6248726 0.00322 ** NA
LTC4S 0.0032514 0.6248726 0.00325 ** NA
MAP2K3 0.0032831 0.6248726 0.00328 ** NA
DIS3L2 0.0032847 0.6248726 0.00328 ** NA
PTPRE 0.0032929 0.6248726 0.00329 ** NA
CNOT1 0.0032940 0.6248726 0.00329 ** NA
ANKRD32 0.0034117 0.6248726 0.00341 ** NA
SCG2 0.0034839 0.6248726 0.00348 ** NA
TRIM8 0.0034895 0.6248726 0.00349 ** NA
CAMK2B 0.0035496 0.6248726 0.00355 ** NA
ETAA1 0.0035617 0.6248726 0.00356 ** NA
ST6GALNAC1 0.0035708 0.6248726 0.00357 ** NA
LILRB2 0.0036287 0.6248726 0.00363 ** NA
SLC38A7 0.0036411 0.6248726 0.00364 ** NA
SOX9 0.0036519 0.6248726 0.00365 ** NA
MMRN2 0.0036540 0.6248726 0.00365 ** NA
MAML2 0.0037079 0.6248726 0.00371 ** NA
POLG 0.0037209 0.6248726 0.00372 ** NA
SGCD 0.0037645 0.6248726 0.00376 ** NA
KRT83 0.0038147 0.6248726 0.00381 ** NA
RB1 0.0038228 0.6248726 0.00382 ** NA
SPAG17 0.0038410 0.6248726 0.00384 ** NA
EEF2 0.0038473 0.6248726 0.00385 ** NA
SUSD5 0.0039261 0.6248726 0.00393 ** NA
ATR 0.0039338 0.6248726 0.00393 ** NA
ATXN2L 0.0041364 0.6248726 0.00414 ** NA
CD83 0.0041819 0.6248726 0.00418 ** NA
TOP1MT 0.0042412 0.6248726 0.00424 ** NA
EMILIN3 0.0042659 0.6248726 0.00427 ** NA
SPHK2 0.0043100 0.6248726 0.00431 ** NA
PSMB4 0.0043587 0.6248726 0.00436 ** NA
ACHE 0.0043818 0.6248726 0.00438 ** NA
ZNF135 0.0045686 0.6248726 0.00457 ** NA
TPP1 0.0046303 0.6248726 0.00463 ** NA
NFE2L1 0.0046446 0.6248726 0.00464 ** NA
KHSRP 0.0046808 0.6248726 0.00468 ** NA
PPP1R1C 0.0047398 0.6248726 0.00474 ** NA
CDC37 0.0047810 0.6248726 0.00478 ** NA
CD200R1 0.0048862 0.6248726 0.00489 ** NA
CWH43 0.0048913 0.6248726 0.00489 ** NA
FOXRED1 0.0049144 0.6248726 0.00491 ** NA
HCRTR1 0.0049160 0.6248726 0.00492 ** NA
ZC3H4 0.0050449 0.6248726 0.00504 ** NA
RBBP9 0.0050826 0.6248726 0.00508 ** NA
OGFOD3 0.0051338 0.6248726 0.00513 ** NA
TBC1D22A 0.0052429 0.6248726 0.00524 ** NA
APOBEC1 0.0052779 0.6248726 0.00528 ** NA
OR56A3 0.0052916 0.6248726 0.00529 ** NA
SLC46A2 0.0053108 0.6248726 0.00531 ** NA
STK17B 0.0053176 0.6248726 0.00532 ** NA
PIGP 0.0053363 0.6248726 0.00534 ** NA
C6orf15 0.0053410 0.6248726 0.00534 ** NA
SMPD1 0.0053513 0.6248726 0.00535 ** NA
RNF43 0.0053669 0.6248726 0.00537 ** NA
FBXW12 0.0053691 0.6248726 0.00537 ** NA
RSPH6A 0.0053903 0.6248726 0.00539 ** NA
THADA 0.0055256 0.6248726 0.00553 ** NA
MYOC 0.0055382 0.6248726 0.00554 ** NA
SLC12A4 0.0056227 0.6248726 0.00562 ** NA
BTAF1 0.0056982 0.6248726 0.00570 ** NA
DNAJC5B 0.0057016 0.6248726 0.00570 ** NA
TAS2R60 0.0057034 0.6248726 0.00570 ** NA
IBA57 0.0057420 0.6248726 0.00574 ** NA
HSD3B7 0.0057475 0.6248726 0.00575 ** NA
HDGFRP2 0.0057596 0.6248726 0.00576 ** NA
MRPS34 0.0057928 0.6248726 0.00579 ** NA
SSH2 0.0058405 0.6248726 0.00584 ** NA
MBD3L2 0.0058844 0.6248726 0.00588 ** NA
COPS2 0.0059315 0.6248726 0.00593 ** NA
WDR75 0.0059969 0.6248726 0.00600 ** NA
FRYL 0.0060235 0.6248726 0.00602 ** NA
MUS81 0.0061273 0.6248726 0.00613 ** NA
CD5L 0.0061772 0.6248726 0.00618 ** NA
DPP7 0.0061926 0.6248726 0.00619 ** NA
LMX1A 0.0062097 0.6248726 0.00621 ** NA
ANTXRL 0.0062215 0.6248726 0.00622 ** NA
MMS22L 0.0062232 0.6248726 0.00622 ** NA
SFXN3 0.0062354 0.6248726 0.00624 ** NA
USP32 0.0062588 0.6248726 0.00626 ** NA
FAM181B 0.0062849 0.6248726 0.00628 ** NA
SMARCB1 0.0062993 0.6248726 0.00630 ** NA
ACBD6 0.0063036 0.6248726 0.00630 ** NA
SUPT7L 0.0063204 0.6248726 0.00632 ** NA
CYP19A1 0.0063281 0.6248726 0.00633 ** NA
C12orf4 0.0063501 0.6248726 0.00635 ** NA
FBXL20 0.0064106 0.6248726 0.00641 ** NA
NXPH4 0.0064264 0.6248726 0.00643 ** NA
CCNB1 0.0065022 0.6248726 0.00650 ** NA
LRGUK 0.0065445 0.6248726 0.00654 ** NA
OR4N2 0.0065455 0.6248726 0.00655 ** NA
RGS12 0.0065563 0.6248726 0.00656 ** NA
CEP76 0.0066070 0.6248726 0.00661 ** NA
MIIP 0.0066145 0.6248726 0.00661 ** NA
FCN3 0.0066248 0.6248726 0.00662 ** NA
ZNF107 0.0066752 0.6248726 0.00668 ** NA
TACSTD2 0.0066861 0.6248726 0.00669 ** NA
GLG1 0.0066950 0.6248726 0.00670 ** NA
TSPAN13 0.0067540 0.6248726 0.00675 ** NA
VPS13D 0.0067657 0.6248726 0.00677 ** NA
LPCAT4 0.0067758 0.6248726 0.00678 ** NA
ATXN2 0.0068586 0.6248726 0.00686 ** NA
DENND4C 0.0068602 0.6248726 0.00686 ** NA
ARID1B 0.0068718 0.6248726 0.00687 ** NA
KDM5B 0.0068837 0.6248726 0.00688 ** NA
IFFO2 0.0068857 0.6248726 0.00689 ** NA
COG3 0.0069083 0.6248726 0.00691 ** NA
DHX35 0.0069214 0.6248726 0.00692 ** NA
PPM1A 0.0069353 0.6248726 0.00694 ** NA
MSGN1 0.0069483 0.6248726 0.00695 ** NA
POC1A 0.0071368 0.6341224 0.00714 ** NA
CLDN5 0.0071709 0.6341224 0.00717 ** NA
PRKCZ 0.0072554 0.6341224 0.00726 ** NA
ACTL8 0.0072969 0.6341224 0.00730 ** NA
MGA 0.0073204 0.6341224 0.00732 ** NA
GTF3C1 0.0073309 0.6341224 0.00733 ** NA
SMCR8 0.0073359 0.6341224 0.00734 ** NA
HSPB2 0.0073660 0.6341224 0.00737 ** NA
EML1 0.0073834 0.6341224 0.00738 ** NA
INTS12 0.0075473 0.6405098 0.00755 ** NA
NAA15 0.0075638 0.6405098 0.00756 ** NA
SPTBN4 0.0075987 0.6405098 0.00760 ** NA
ARSB 0.0076069 0.6405098 0.00761 ** NA
VPS37B 0.0076704 0.6427055 0.00767 ** NA
CREBBP 0.0077132 0.6431501 0.00771 ** NA
NCR3LG1 0.0078580 0.6514880 0.00786 ** NA
ZNF493 0.0078890 0.6514880 0.00789 ** NA
AP5B1 0.0079487 0.6523618 0.00795 ** NA
ACYP2 0.0079841 0.6523618 0.00798 ** NA
C20orf26 0.0080463 0.6523618 0.00805 ** NA
PLOD1 0.0080515 0.6523618 0.00805 ** NA
OR4S2 0.0081035 0.6534954 0.00810 ** NA
SPO11 0.0081886 0.6568749 0.00819 ** NA
MCTP1 0.0082300 0.6568749 0.00823 ** NA
ELK3 0.0083459 0.6568749 0.00835 ** NA
H2AFY2 0.0083475 0.6568749 0.00835 ** NA
MRPL13 0.0083762 0.6568749 0.00838 ** NA
PAQR3 0.0084147 0.6568749 0.00841 ** NA
GHITM 0.0084474 0.6568749 0.00845 ** NA
ADO 0.0085378 0.6568749 0.00854 ** NA
MFAP4 0.0085654 0.6568749 0.00857 ** NA
CD163 0.0085663 0.6568749 0.00857 ** NA
ARMC7 0.0085988 0.6568749 0.00860 ** NA
COL8A1 0.0086043 0.6568749 0.00860 ** NA
NBPF15 0.0086920 0.6606339 0.00869 ** NA
OR13C3 0.0088398 0.6659977 0.00884 ** NA
COL7A1 0.0088402 0.6659977 0.00884 ** NA
C1orf141 0.0089498 0.6698168 0.00895 ** NA
ARHGEF17 0.0089894 0.6698168 0.00899 ** NA
COL9A2 0.0090078 0.6698168 0.00901 ** NA
DTHD1 0.0090748 0.6699801 0.00907 ** NA
LRP12 0.0090984 0.6699801 0.00910 ** NA
CCNL1 0.0091597 0.6699801 0.00916 ** NA
TMEM258 0.0091769 0.6699801 0.00918 ** NA
DNAJC8 0.0093058 0.6699801 0.00931 ** NA
KRTAP4-4 0.0093494 0.6699801 0.00935 ** NA
LCTL 0.0093837 0.6699801 0.00938 ** NA
ELMSAN1 0.0093954 0.6699801 0.00940 ** NA
SGK2 0.0093988 0.6699801 0.00940 ** NA
SLC25A4 0.0094344 0.6699801 0.00943 ** NA
F11R 0.0094708 0.6699801 0.00947 ** NA
LY6G6F 0.0095144 0.6699801 0.00951 ** NA
ZNF330 0.0095171 0.6699801 0.00952 ** NA
NECAB1 0.0095689 0.6708810 0.00957 ** NA
GMCL1 0.0096195 0.6710890 0.00962 ** NA
C16orf58 0.0096729 0.6710890 0.00967 ** NA
ZBTB44 0.0096904 0.6710890 0.00969 ** NA
INHBA 0.0097282 0.6710890 0.00973 ** NA
RNF31 0.0098119 0.6727895 0.00981 ** NA
CACNA1I 0.0098312 0.6727895 0.00983 ** NA
COPS3 0.0098996 0.6747558 0.00990 ** NA
NOL6 0.0099717 0.6747558 0.00997 ** NA
WDR77 0.0099778 0.6747558 0.00998 ** NA
wilcox_gene_nonsilent_plot <- ggplot(data = crispr_signif_nonsilent) +
  geom_histogram(aes(x = p, fill = "chartreuse4"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  geom_histogram(aes(x = p.adj, fill = "darkslategray3"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  scale_x_continuous(breaks = seq(0, 1, by = 0.05), labels = seq(0, 1, by = 0.05)) +
  scale_fill_manual(name = "P-values", values = c("chartreuse4" = "chartreuse4", "darkslategray3" = "darkslategray3"), labels = c("Unadjusted", "BH-adjusted")) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = c(0.1, 0.85)) +
  labs(x = "BH-adjusted p-values", y = "Frequency")
wilcox_gene_nonsilent_plot

4.1.3 Deleterious and missense mutant vs. Other

crispr_signif_delmis <- compare_means(Score ~ Mutation_Status_DeleteriousMissense, group.by = c("Hugo_Symbol"), data = crispr_data_ptmuts, method = "wilcox.test", p.adjust.method = "BH")
crispr_signif_delmis <- adj_signif(crispr_signif_delmis)
crispr_signif_delmis <- crispr_signif_delmis[order(crispr_signif_delmis$p),]
saveRDS(crispr_signif_delmis, "./data_munging/rds/crispr_signif_ptmuts_deleteriousmissense_gene.rds")

# write.table(crispr_signif_delmis, file = "~/Desktop/crispr_signif_ptmuts_deleteriousmissense_gene.csv", quote = FALSE, sep = ",", row.names = FALSE)
crispr_signif_delmis <- readRDS("./data_munging/rds/crispr_signif_ptmuts_deleteriousmissense_gene.rds")

knitr::kable(filter(crispr_signif_delmis, p < 0.01)[, c("Hugo_Symbol", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "Wilcoxon test results comparing deleterious and missense mutant vs other cell lines, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
Wilcoxon test results comparing deleterious and missense mutant vs other cell lines, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Hugo_Symbol p p.adj p.format p.signif p.signif.adj
KRAS 0.0000000 0.0000000 < 2e-16 **** ****
TP53 0.0000000 0.0000000 < 2e-16 **** ****
NRAS 0.0000000 0.0000000 < 2e-16 **** ****
BRAF 0.0000000 0.0000000 < 2e-16 **** ****
PTEN 0.0000000 0.0000000 2.1e-15 **** ****
PIK3CA 0.0000000 0.0000000 9.6e-14 **** ****
CTNNB1 0.0000058 0.0143296 5.8e-06 ****
TCERG1 0.0000135 0.0290301 1.4e-05 ****
ARID1A 0.0000332 0.0632941 3.3e-05 **** NA
FCGBP 0.0000696 0.1194946 7.0e-05 **** NA
TPR 0.0000827 0.1291821 8.3e-05 **** NA
SLC22A9 0.0002503 0.3498147 0.00025 *** NA
GSTM5 0.0002648 0.3498147 0.00026 *** NA
CPSF1 0.0003055 0.3748046 0.00031 *** NA
PIGW 0.0003638 0.3925692 0.00036 *** NA
C14orf39 0.0003657 0.3925692 0.00037 *** NA
TLX2 0.0004787 0.4540312 0.00048 *** NA
NIPBL 0.0005034 0.4540312 0.00050 *** NA
ARFGAP1 0.0005157 0.4540312 0.00052 *** NA
ZNF808 0.0005323 0.4540312 0.00053 *** NA
TAOK2 0.0005756 0.4540312 0.00058 *** NA
CD320 0.0005816 0.4540312 0.00058 *** NA
MAT2B 0.0007184 0.5125383 0.00072 *** NA
ZNF177 0.0007543 0.5125383 0.00075 *** NA
SYNPO2L 0.0007699 0.5125383 0.00077 *** NA
HRAS 0.0008408 0.5125383 0.00084 *** NA
UTP3 0.0008549 0.5125383 0.00085 *** NA
PIK3R1 0.0008568 0.5125383 0.00086 *** NA
RNF208 0.0008907 0.5125383 0.00089 *** NA
FBXW7 0.0008953 0.5125383 0.00090 *** NA
KCNIP4 0.0009472 0.5247519 0.00095 *** NA
GOLGA3 0.0010340 0.5296229 0.00103 ** NA
DSEL 0.0010395 0.5296229 0.00104 ** NA
PROM1 0.0010485 0.5296229 0.00105 ** NA
PMPCA 0.0010990 0.5392650 0.00110 ** NA
OR52E8 0.0012095 0.5479916 0.00121 ** NA
OR13C4 0.0012702 0.5479916 0.00127 ** NA
TELO2 0.0012922 0.5479916 0.00129 ** NA
TAS1R2 0.0013163 0.5479916 0.00132 ** NA
ZNF439 0.0013227 0.5479916 0.00132 ** NA
MDN1 0.0013858 0.5479916 0.00139 ** NA
MTMR2 0.0014414 0.5479916 0.00144 ** NA
E2F1 0.0014486 0.5479916 0.00145 ** NA
LPO 0.0014636 0.5479916 0.00146 ** NA
ING2 0.0014811 0.5479916 0.00148 ** NA
VHL 0.0014920 0.5479916 0.00149 ** NA
PLA2G4F 0.0014997 0.5479916 0.00150 ** NA
MEF2C 0.0015423 0.5512411 0.00154 ** NA
UTP20 0.0015804 0.5512411 0.00158 ** NA
IQCH 0.0016077 0.5512411 0.00161 ** NA
PCDHB15 0.0016370 0.5512411 0.00164 ** NA
ACSM2B 0.0017107 0.5584212 0.00171 ** NA
OR8D1 0.0017233 0.5584212 0.00172 ** NA
RFC1 0.0018888 0.5989004 0.00189 ** NA
KIAA1211L 0.0019402 0.5989004 0.00194 ** NA
SENP8 0.0019559 0.5989004 0.00196 ** NA
RUVBL1 0.0019950 0.5989004 0.00200 ** NA
CLEC4C 0.0020667 0.5989004 0.00207 ** NA
PCDHA8 0.0020705 0.5989004 0.00207 ** NA
PI4K2A 0.0020961 0.5989004 0.00210 ** NA
KPNA6 0.0021334 0.5989004 0.00213 ** NA
POU2F1 0.0022102 0.5989004 0.00221 ** NA
EZH2 0.0022226 0.5989004 0.00222 ** NA
BTBD11 0.0022318 0.5989004 0.00223 ** NA
ANKRD31 0.0023475 0.6075732 0.00235 ** NA
OR51T1 0.0023614 0.6075732 0.00236 ** NA
METTL17 0.0023923 0.6075732 0.00239 ** NA
CIITA 0.0024057 0.6075732 0.00241 ** NA
ADAM15 0.0024806 0.6174244 0.00248 ** NA
TUBB2A 0.0025373 0.6182473 0.00254 ** NA
TAAR1 0.0025653 0.6182473 0.00257 ** NA
ZDHHC24 0.0025919 0.6182473 0.00259 ** NA
SURF6 0.0027361 0.6329390 0.00274 ** NA
KIF11 0.0027512 0.6329390 0.00275 ** NA
GRM7 0.0027641 0.6329390 0.00276 ** NA
OLFML2B 0.0029959 0.6347331 0.00300 ** NA
SAMM50 0.0030102 0.6347331 0.00301 ** NA
ACTA1 0.0030465 0.6347331 0.00305 ** NA
FRMD4B 0.0030803 0.6347331 0.00308 ** NA
SLC26A2 0.0030940 0.6347331 0.00309 ** NA
MYH13 0.0032183 0.6347331 0.00322 ** NA
LTC4S 0.0032514 0.6347331 0.00325 ** NA
MAP2K3 0.0032831 0.6347331 0.00328 ** NA
DIS3L2 0.0032847 0.6347331 0.00328 ** NA
PTPRE 0.0032929 0.6347331 0.00329 ** NA
CNOT1 0.0032940 0.6347331 0.00329 ** NA
ANKRD32 0.0034117 0.6347331 0.00341 ** NA
SCG2 0.0034839 0.6347331 0.00348 ** NA
TRIM8 0.0034895 0.6347331 0.00349 ** NA
CAMK2B 0.0035496 0.6347331 0.00355 ** NA
ETAA1 0.0035617 0.6347331 0.00356 ** NA
ST6GALNAC1 0.0035708 0.6347331 0.00357 ** NA
SLC38A7 0.0036411 0.6347331 0.00364 ** NA
SOX9 0.0036519 0.6347331 0.00365 ** NA
MMRN2 0.0036540 0.6347331 0.00365 ** NA
SGCD 0.0037645 0.6347331 0.00376 ** NA
KRT83 0.0038147 0.6347331 0.00381 ** NA
RB1 0.0038228 0.6347331 0.00382 ** NA
SPAG17 0.0038410 0.6347331 0.00384 ** NA
EEF2 0.0038473 0.6347331 0.00385 ** NA
SUSD5 0.0039261 0.6347331 0.00393 ** NA
NOL7 0.0039331 0.6347331 0.00393 ** NA
ATR 0.0039338 0.6347331 0.00393 ** NA
AVEN 0.0041287 0.6347331 0.00413 ** NA
ATXN2L 0.0041364 0.6347331 0.00414 ** NA
CD83 0.0041819 0.6347331 0.00418 ** NA
TOP1MT 0.0042412 0.6347331 0.00424 ** NA
EMILIN3 0.0042659 0.6347331 0.00427 ** NA
SPHK2 0.0043100 0.6347331 0.00431 ** NA
PSMB4 0.0043587 0.6347331 0.00436 ** NA
ACHE 0.0043818 0.6347331 0.00438 ** NA
IQGAP3 0.0045407 0.6347331 0.00454 ** NA
ZNF135 0.0045686 0.6347331 0.00457 ** NA
TPP1 0.0046303 0.6347331 0.00463 ** NA
NFE2L1 0.0046446 0.6347331 0.00464 ** NA
KHSRP 0.0046808 0.6347331 0.00468 ** NA
PPP1R1C 0.0047398 0.6347331 0.00474 ** NA
CDC37 0.0047810 0.6347331 0.00478 ** NA
CD200R1 0.0048862 0.6347331 0.00489 ** NA
CWH43 0.0048913 0.6347331 0.00489 ** NA
FOXRED1 0.0049144 0.6347331 0.00491 ** NA
HCRTR1 0.0049160 0.6347331 0.00492 ** NA
ZC3H4 0.0050449 0.6347331 0.00504 ** NA
RBBP9 0.0050826 0.6347331 0.00508 ** NA
OGFOD3 0.0051338 0.6347331 0.00513 ** NA
TBC1D22A 0.0052429 0.6347331 0.00524 ** NA
APOBEC1 0.0052779 0.6347331 0.00528 ** NA
OR56A3 0.0052916 0.6347331 0.00529 ** NA
SLC46A2 0.0053108 0.6347331 0.00531 ** NA
PIGP 0.0053363 0.6347331 0.00534 ** NA
C6orf15 0.0053410 0.6347331 0.00534 ** NA
SMPD1 0.0053513 0.6347331 0.00535 ** NA
RNF43 0.0053669 0.6347331 0.00537 ** NA
FBXW12 0.0053691 0.6347331 0.00537 ** NA
RSPH6A 0.0053903 0.6347331 0.00539 ** NA
THADA 0.0055256 0.6347331 0.00553 ** NA
MYOC 0.0055382 0.6347331 0.00554 ** NA
SLC12A4 0.0056227 0.6347331 0.00562 ** NA
BTAF1 0.0056982 0.6347331 0.00570 ** NA
DNAJC5B 0.0057016 0.6347331 0.00570 ** NA
TAS2R60 0.0057034 0.6347331 0.00570 ** NA
IBA57 0.0057420 0.6347331 0.00574 ** NA
HSD3B7 0.0057475 0.6347331 0.00575 ** NA
HDGFRP2 0.0057596 0.6347331 0.00576 ** NA
MRPS34 0.0057928 0.6347331 0.00579 ** NA
SSH2 0.0058405 0.6347331 0.00584 ** NA
MBD3L2 0.0058844 0.6347331 0.00588 ** NA
COPS2 0.0059315 0.6347331 0.00593 ** NA
WDR75 0.0059969 0.6347331 0.00600 ** NA
FRYL 0.0060235 0.6347331 0.00602 ** NA
ATXN2 0.0060319 0.6347331 0.00603 ** NA
NBPF15 0.0060598 0.6347331 0.00606 ** NA
MUS81 0.0061273 0.6347331 0.00613 ** NA
CD5L 0.0061772 0.6347331 0.00618 ** NA
DPP7 0.0061926 0.6347331 0.00619 ** NA
LMX1A 0.0062097 0.6347331 0.00621 ** NA
ANTXRL 0.0062215 0.6347331 0.00622 ** NA
MMS22L 0.0062232 0.6347331 0.00622 ** NA
SFXN3 0.0062354 0.6347331 0.00624 ** NA
USP32 0.0062588 0.6347331 0.00626 ** NA
RAB43 0.0062666 0.6347331 0.00627 ** NA
FAM181B 0.0062849 0.6347331 0.00628 ** NA
SMARCB1 0.0062993 0.6347331 0.00630 ** NA
ACBD6 0.0063036 0.6347331 0.00630 ** NA
SUPT7L 0.0063204 0.6347331 0.00632 ** NA
CYP19A1 0.0063281 0.6347331 0.00633 ** NA
C12orf4 0.0063501 0.6347331 0.00635 ** NA
FBXL20 0.0064106 0.6347331 0.00641 ** NA
NXPH4 0.0064264 0.6347331 0.00643 ** NA
CCNB1 0.0065022 0.6347331 0.00650 ** NA
OR4N2 0.0065455 0.6347331 0.00655 ** NA
RGS12 0.0065563 0.6347331 0.00656 ** NA
CEP76 0.0066070 0.6347331 0.00661 ** NA
MIIP 0.0066145 0.6347331 0.00661 ** NA
FCN3 0.0066248 0.6347331 0.00662 ** NA
ZNF107 0.0066752 0.6347331 0.00668 ** NA
TACSTD2 0.0066861 0.6347331 0.00669 ** NA
GLG1 0.0066950 0.6347331 0.00670 ** NA
TSPAN13 0.0067540 0.6347331 0.00675 ** NA
VPS13D 0.0067657 0.6347331 0.00677 ** NA
LPCAT4 0.0067758 0.6347331 0.00678 ** NA
DENND4C 0.0068602 0.6347331 0.00686 ** NA
ARID1B 0.0068718 0.6347331 0.00687 ** NA
KDM5B 0.0068837 0.6347331 0.00688 ** NA
IFFO2 0.0068857 0.6347331 0.00689 ** NA
COG3 0.0069083 0.6347331 0.00691 ** NA
DHX35 0.0069214 0.6347331 0.00692 ** NA
MSGN1 0.0069483 0.6347331 0.00695 ** NA
CLDN5 0.0071709 0.6515991 0.00717 ** NA
PRKCZ 0.0072554 0.6527818 0.00726 ** NA
ACTL8 0.0072969 0.6527818 0.00730 ** NA
MGA 0.0073204 0.6527818 0.00732 ** NA
SMCR8 0.0073359 0.6527818 0.00734 ** NA
EML1 0.0073834 0.6536203 0.00738 ** NA
F11R 0.0075268 0.6564883 0.00753 ** NA
INTS12 0.0075473 0.6564883 0.00755 ** NA
NAA15 0.0075638 0.6564883 0.00756 ** NA
SPTBN4 0.0075987 0.6564883 0.00760 ** NA
ARSB 0.0076069 0.6564883 0.00761 ** NA
VPS37B 0.0076704 0.6586581 0.00767 ** NA
CREBBP 0.0077132 0.6590337 0.00771 ** NA
NCR3LG1 0.0078580 0.6680868 0.00786 ** NA
AP5B1 0.0079487 0.6712453 0.00795 ** NA
ACYP2 0.0079841 0.6712453 0.00798 ** NA
C20orf26 0.0080463 0.6712453 0.00805 ** NA
PLOD1 0.0080515 0.6712453 0.00805 ** NA
OR4S2 0.0081035 0.6723199 0.00810 ** NA
SPO11 0.0081886 0.6747536 0.00819 ** NA
MCTP1 0.0082300 0.6747536 0.00823 ** NA
ELK3 0.0083459 0.6747536 0.00835 ** NA
H2AFY2 0.0083475 0.6747536 0.00835 ** NA
MRPL13 0.0083762 0.6747536 0.00838 ** NA
PAQR3 0.0084147 0.6747536 0.00841 ** NA
GHITM 0.0084474 0.6747536 0.00845 ** NA
ADO 0.0085378 0.6747536 0.00854 ** NA
MFAP4 0.0085654 0.6747536 0.00857 ** NA
CD163 0.0085663 0.6747536 0.00857 ** NA
ARMC7 0.0085988 0.6747536 0.00860 ** NA
COL8A1 0.0086043 0.6747536 0.00860 ** NA
LILRB2 0.0088241 0.6822660 0.00882 ** NA
OR13C3 0.0088398 0.6822660 0.00884 ** NA
COL7A1 0.0088402 0.6822660 0.00884 ** NA
C1orf141 0.0089498 0.6822660 0.00895 ** NA
ARHGEF17 0.0089894 0.6822660 0.00899 ** NA
COL9A2 0.0090078 0.6822660 0.00901 ** NA
DTHD1 0.0090748 0.6822660 0.00907 ** NA
H3F3A 0.0090755 0.6822660 0.00908 ** NA
LRP12 0.0090984 0.6822660 0.00910 ** NA
CCNL1 0.0091597 0.6822660 0.00916 ** NA
MAML2 0.0091672 0.6822660 0.00917 ** NA
TMEM258 0.0091769 0.6822660 0.00918 ** NA
DNAJC8 0.0093058 0.6835655 0.00931 ** NA
KRTAP4-4 0.0093494 0.6835655 0.00935 ** NA
LCTL 0.0093837 0.6835655 0.00938 ** NA
SGK2 0.0093988 0.6835655 0.00940 ** NA
SLC25A4 0.0094344 0.6835655 0.00943 ** NA
LY6G6F 0.0095144 0.6835655 0.00951 ** NA
ZNF330 0.0095171 0.6835655 0.00952 ** NA
NECAB1 0.0095689 0.6835655 0.00957 ** NA
PPM1A 0.0095782 0.6835655 0.00958 ** NA
GMCL1 0.0096195 0.6835655 0.00962 ** NA
C16orf58 0.0096729 0.6835655 0.00967 ** NA
ZBTB44 0.0096904 0.6835655 0.00969 ** NA
INHBA 0.0097282 0.6835655 0.00973 ** NA
GTF3C1 0.0097721 0.6835655 0.00977 ** NA
RNF31 0.0098119 0.6835655 0.00981 ** NA
CACNA1I 0.0098312 0.6835655 0.00983 ** NA
COPS3 0.0098996 0.6854321 0.00990 ** NA
NOL6 0.0099717 0.6854321 0.00997 ** NA
WDR77 0.0099778 0.6854321 0.00998 ** NA
wilcox_gene_delmis_plot <- ggplot(data = crispr_signif_delmis) +
  geom_histogram(aes(x = p, fill = "chartreuse4"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  geom_histogram(aes(x = p.adj, fill = "darkslategray3"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  scale_x_continuous(breaks = seq(0, 1, by = 0.05), labels = seq(0, 1, by = 0.05)) +
  scale_fill_manual(name = "P-values", values = c("chartreuse4" = "chartreuse4", "darkslategray3" = "darkslategray3"), labels = c("Unadjusted", "BH-adjusted")) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = c(0.1, 0.85)) +
  labs(x = "BH-adjusted p-values", y = "Frequency")
wilcox_gene_delmis_plot

4.2 Grouped by gene and lineage

4.2.1 Deleterious vs. Other

crispr_signif_del_lineage <- compare_means(Score ~ Mutation_Status_Deleterious, group.by = c("Hugo_Symbol", "group_general_lineage_name"), data = crispr_data_ptmuts, method = "wilcox.test", p.adjust.method = "BH")
crispr_signif_del_lineage <- adj_signif(crispr_signif_del_lineage)
crispr_signif_del_lineage <- crispr_signif_del_lineage[order(crispr_signif_del_lineage$p),]
saveRDS(crispr_signif_del_lineage, "./data_munging/rds/crispr_signif_ptmuts_deleterious_lineage.rds")

# write.table(crispr_signif_del_lineage, file = "~/Desktop/crispr_signif_ptmuts_deleterious_lineage.csv", quote = FALSE, sep = ",", row.names = FALSE)
crispr_signif_del_lineage <- readRDS("./data_munging/rds/crispr_signif_ptmuts_deleterious_lineage.rds")

knitr::kable(filter(crispr_signif_del_lineage, p < 0.01)[, c("Hugo_Symbol", "group_general_lineage_name", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "Wilcoxon test results comparing deleterious mutant vs other cell lines by lineage, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
Wilcoxon test results comparing deleterious mutant vs other cell lines by lineage, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Hugo_Symbol group_general_lineage_name p p.adj p.format p.signif p.signif.adj
PTEN central nervous system cancer 0.0006658 0.8370371 0.00067 *** NA
TP53 lung cancer 0.0007958 0.8370371 0.00080 *** NA
DCAF8 lung cancer 0.0033009 0.8370371 0.00330 ** NA
PTEN ovarian cancer 0.0043470 0.8370371 0.00435 ** NA
SETD2 kidney cancer 0.0056647 0.8370371 0.00566 ** NA
DVL2 uterine cancer 0.0067676 0.8370371 0.00677 ** NA
ARID1A pancreatic cancer 0.0076726 0.8370371 0.00767 ** NA
MCPH1 lung cancer 0.0077467 0.8370371 0.00775 ** NA
wilcox_lineage_plot <- ggplot(data = crispr_signif_del_lineage) +
  geom_histogram(aes(x = p, fill = "chartreuse4"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  geom_histogram(aes(x = p.adj, fill = "darkslategray3"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  scale_x_continuous(breaks = seq(0, 1, by = 0.05), labels = seq(0, 1, by = 0.05)) +
  scale_fill_manual(name = "P-values", values = c("chartreuse4" = "chartreuse4", "darkslategray3" = "darkslategray3"), labels = c("Unadjusted", "BH-adjusted")) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = c(0.1, 0.85)) +
  labs(x = "BH-adjusted p-values", y = "Frequency")
wilcox_lineage_plot

4.2.2 Non-silent mutant vs. Other

crispr_signif_nonsilent_lineage <- compare_means(Score ~ Mutation_Status_Nonsilent, group.by = c("Hugo_Symbol", "group_general_lineage_name"), data = crispr_data_ptmuts, method = "wilcox.test", p.adjust.method = "BH")
crispr_signif_nonsilent_lineage <- adj_signif(crispr_signif_nonsilent_lineage)
crispr_signif_nonsilent_lineage <- crispr_signif_nonsilent_lineage[order(crispr_signif_nonsilent_lineage$p),]
saveRDS(crispr_signif_nonsilent_lineage, "./data_munging/rds/crispr_signif_ptmuts_nonsilent_lineage.rds")

# write.table(crispr_signif_nonsilent_lineage, file = "~/Desktop/crispr_signif_ptmuts_nonsilent_lineage.csv", quote = FALSE, sep = ",", row.names = FALSE)
crispr_signif_nonsilent_lineage <- readRDS("./data_munging/rds/crispr_signif_ptmuts_nonsilent_lineage.rds")

knitr::kable(filter(crispr_signif_nonsilent_lineage, p < 0.01)[, c("Hugo_Symbol", "group_general_lineage_name", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "Wilcoxon test results comparing non-silent mutant vs other cell lines by lineage, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
Wilcoxon test results comparing non-silent mutant vs other cell lines by lineage, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Hugo_Symbol group_general_lineage_name p p.adj p.format p.signif p.signif.adj
KRAS lung cancer 0.0000000 0.0003131 2.5e-09 **** ***
TP53 lung cancer 0.0000092 0.5639398 9.2e-06 **** NA
TP53 central nervous system cancer 0.0000386 0.8671069 3.9e-05 **** NA
KRAS ovarian cancer 0.0001488 0.8671069 0.00015 *** NA
TP53 ovarian cancer 0.0002344 0.8671069 0.00023 *** NA
NRAS leukemia 0.0005316 0.8671069 0.00053 *** NA
PIK3CA ovarian cancer 0.0008371 0.8671069 0.00084 *** NA
DZANK1 breast cancer 0.0009819 0.8671069 0.00098 *** NA
PIK3CA breast cancer 0.0009842 0.8671069 0.00098 *** NA
KRAS colorectal cancer 0.0010547 0.8671069 0.00105 ** NA
TP53 leukemia 0.0010605 0.8671069 0.00106 ** NA
UNC45B lung cancer 0.0011215 0.8671069 0.00112 ** NA
GOLGA3 uterine cancer 0.0013666 0.8671069 0.00137 ** NA
NRAS multiple myeloma 0.0013756 0.8671069 0.00138 ** NA
GTF3C1 lung cancer 0.0013893 0.8671069 0.00139 ** NA
TICRR uterine cancer 0.0014839 0.8671069 0.00148 ** NA
ARID1A pancreatic cancer 0.0014920 0.8671069 0.00149 ** NA
NRAS skin cancer 0.0016637 0.8671069 0.00166 ** NA
HYOU1 colorectal cancer 0.0018107 0.8671069 0.00181 ** NA
USP34 breast cancer 0.0018820 0.8671069 0.00188 ** NA
MGA uterine cancer 0.0019495 0.8671069 0.00195 ** NA
TNRC6A lung cancer 0.0023744 0.8671069 0.00237 ** NA
SNX29 uterine cancer 0.0026104 0.8671069 0.00261 ** NA
KMT2B breast cancer 0.0026433 0.8671069 0.00264 ** NA
TP53 kidney cancer 0.0027562 0.8671069 0.00276 ** NA
GSPT1 colorectal cancer 0.0028115 0.8671069 0.00281 ** NA
AP1G2 breast cancer 0.0029835 0.8671069 0.00298 ** NA
ZNF264 colorectal cancer 0.0029879 0.8671069 0.00299 ** NA
PTEN ovarian cancer 0.0032141 0.8671069 0.00321 ** NA
KMT2B ovarian cancer 0.0032684 0.8671069 0.00327 ** NA
KIAA0586 colorectal cancer 0.0032838 0.8671069 0.00328 ** NA
ANK2 colorectal cancer 0.0033597 0.8671069 0.00336 ** NA
TGS1 uterine cancer 0.0035864 0.8671069 0.00359 ** NA
COL2A1 leukemia 0.0036054 0.8671069 0.00361 ** NA
HYDIN colorectal cancer 0.0036704 0.8671069 0.00367 ** NA
TP53 colorectal cancer 0.0036994 0.8671069 0.00370 ** NA
TP53 skin cancer 0.0037227 0.8671069 0.00372 ** NA
VHL kidney cancer 0.0037539 0.8671069 0.00375 ** NA
PAPPA uterine cancer 0.0037971 0.8671069 0.00380 ** NA
ZNF292 lung cancer 0.0038193 0.8671069 0.00382 ** NA
PTEN central nervous system cancer 0.0038648 0.8671069 0.00386 ** NA
NEBL uterine cancer 0.0039522 0.8671069 0.00395 ** NA
KRAS stomach cancer 0.0040001 0.8671069 0.00400 ** NA
MTMR3 lung cancer 0.0041192 0.8671069 0.00412 ** NA
TSKS uterine cancer 0.0042550 0.8671069 0.00425 ** NA
ARHGAP12 colorectal cancer 0.0042860 0.8671069 0.00429 ** NA
PAM uterine cancer 0.0043488 0.8671069 0.00435 ** NA
PDE10A uterine cancer 0.0043488 0.8671069 0.00435 ** NA
MAML2 uterine cancer 0.0044510 0.8671069 0.00445 ** NA
NCOR2 colorectal cancer 0.0046575 0.8671069 0.00466 ** NA
VAV3 lung cancer 0.0047769 0.8671069 0.00478 ** NA
SYNE1 lung cancer 0.0047800 0.8671069 0.00478 ** NA
NUP88 uterine cancer 0.0048888 0.8671069 0.00489 ** NA
LRRIQ1 central nervous system cancer 0.0049271 0.8671069 0.00493 ** NA
SVOPL lung cancer 0.0049276 0.8671069 0.00493 ** NA
PTEN uterine cancer 0.0049419 0.8671069 0.00494 ** NA
BRAF skin cancer 0.0049885 0.8671069 0.00499 ** NA
AXIN1 ovarian cancer 0.0050354 0.8671069 0.00504 ** NA
TNK2 uterine cancer 0.0050864 0.8671069 0.00509 ** NA
DIP2C kidney cancer 0.0052747 0.8671069 0.00527 ** NA
CTAGE15 lung cancer 0.0053375 0.8671069 0.00534 ** NA
LSP1 colorectal cancer 0.0053452 0.8671069 0.00535 ** NA
CORIN colorectal cancer 0.0054756 0.8671069 0.00548 ** NA
SETD2 kidney cancer 0.0056647 0.8671069 0.00566 ** NA
FNDC7 colorectal cancer 0.0057059 0.8671069 0.00571 ** NA
SKOR1 uterine cancer 0.0057795 0.8671069 0.00578 ** NA
PARD3B uterine cancer 0.0059302 0.8671069 0.00593 ** NA
HTR7 lung cancer 0.0059492 0.8671069 0.00595 ** NA
FNIP1 ovarian cancer 0.0060557 0.8671069 0.00606 ** NA
AP2A2 breast cancer 0.0061570 0.8671069 0.00616 ** NA
ASXL1 colorectal cancer 0.0061648 0.8671069 0.00616 ** NA
CCT8L2 colorectal cancer 0.0061648 0.8671069 0.00616 ** NA
MTMR14 colorectal cancer 0.0061648 0.8671069 0.00616 ** NA
TTC32 uterine cancer 0.0062881 0.8671069 0.00629 ** NA
PIK3R1 ovarian cancer 0.0062895 0.8671069 0.00629 ** NA
NR4A1 lung cancer 0.0064934 0.8671069 0.00649 ** NA
SLC22A17 lung cancer 0.0064934 0.8671069 0.00649 ** NA
FAM208B colorectal cancer 0.0065065 0.8671069 0.00651 ** NA
ROCK1 colorectal cancer 0.0066106 0.8671069 0.00661 ** NA
ZNF521 colorectal cancer 0.0066975 0.8671069 0.00670 ** NA
RFWD3 lung cancer 0.0067488 0.8671069 0.00675 ** NA
TYRP1 lung cancer 0.0067488 0.8671069 0.00675 ** NA
ADRBK2 uterine cancer 0.0067676 0.8671069 0.00677 ** NA
DVL2 uterine cancer 0.0067676 0.8671069 0.00677 ** NA
EXOC8 uterine cancer 0.0067676 0.8671069 0.00677 ** NA
ITSN1 colorectal cancer 0.0069403 0.8671069 0.00694 ** NA
ZNF292 colorectal cancer 0.0069403 0.8671069 0.00694 ** NA
MIS18BP1 colorectal cancer 0.0070098 0.8671069 0.00701 ** NA
OR5M8 colorectal cancer 0.0070184 0.8671069 0.00702 ** NA
LILRB2 leukemia 0.0070255 0.8671069 0.00703 ** NA
PIK3CA colorectal cancer 0.0070506 0.8671069 0.00705 ** NA
DDX11 lung cancer 0.0070952 0.8671069 0.00710 ** NA
KMT2C multiple myeloma 0.0071541 0.8671069 0.00715 ** NA
MYH6 uterine cancer 0.0071786 0.8671069 0.00718 ** NA
ARID1B ovarian cancer 0.0072055 0.8671069 0.00721 ** NA
SURF6 uterine cancer 0.0073058 0.8671069 0.00731 ** NA
C1orf86 leukemia 0.0073710 0.8671069 0.00737 ** NA
TEX10 ovarian cancer 0.0075601 0.8671069 0.00756 ** NA
ALPK3 ovarian cancer 0.0076300 0.8671069 0.00763 ** NA
MYOM3 lung cancer 0.0076435 0.8671069 0.00764 ** NA
PNPLA5 colorectal cancer 0.0077740 0.8671069 0.00777 ** NA
PTGFRN lung cancer 0.0078691 0.8671069 0.00787 ** NA
PCDH10 uterine cancer 0.0078925 0.8671069 0.00789 ** NA
ANKRD23 colorectal cancer 0.0078965 0.8671069 0.00790 ** NA
HERC1 skin cancer 0.0079501 0.8671069 0.00795 ** NA
C12orf4 uterine cancer 0.0080215 0.8671069 0.00802 ** NA
EGFLAM lung cancer 0.0081906 0.8671069 0.00819 ** NA
SPRED1 lung cancer 0.0084512 0.8671069 0.00845 ** NA
SBF2 lung cancer 0.0084541 0.8671069 0.00845 ** NA
EFCAB5 ovarian cancer 0.0084624 0.8671069 0.00846 ** NA
CSNK1D uterine cancer 0.0085754 0.8671069 0.00858 ** NA
FAM110A uterine cancer 0.0085754 0.8671069 0.00858 ** NA
MICAL1 uterine cancer 0.0085754 0.8671069 0.00858 ** NA
MORF4L1 uterine cancer 0.0085754 0.8671069 0.00858 ** NA
RIOK2 uterine cancer 0.0085754 0.8671069 0.00858 ** NA
GAA colorectal cancer 0.0085960 0.8671069 0.00860 ** NA
KIAA1107 colorectal cancer 0.0085960 0.8671069 0.00860 ** NA
ZNF536 skin cancer 0.0086987 0.8671069 0.00870 ** NA
RPS6KA2 uterine cancer 0.0087779 0.8671069 0.00878 ** NA
SOGA1 lung cancer 0.0088239 0.8671069 0.00882 ** NA
TPR lung cancer 0.0088831 0.8671069 0.00888 ** NA
TECRL lung cancer 0.0089331 0.8671069 0.00893 ** NA
SVEP1 skin cancer 0.0090238 0.8671069 0.00902 ** NA
DPYSL5 uterine cancer 0.0091517 0.8671069 0.00915 ** NA
FAM189A1 uterine cancer 0.0091517 0.8671069 0.00915 ** NA
HSPB2 uterine cancer 0.0091517 0.8671069 0.00915 ** NA
IRX6 uterine cancer 0.0091517 0.8671069 0.00915 ** NA
XKR9 uterine cancer 0.0091517 0.8671069 0.00915 ** NA
KIAA0922 lung cancer 0.0091532 0.8671069 0.00915 ** NA
STK17B lung cancer 0.0092125 0.8671069 0.00921 ** NA
TTF1 lung cancer 0.0092125 0.8671069 0.00921 ** NA
TBC1D10B uterine cancer 0.0092568 0.8671069 0.00926 ** NA
DNAJC8 uterine cancer 0.0092717 0.8671069 0.00927 ** NA
PKDCC uterine cancer 0.0092717 0.8671069 0.00927 ** NA
TCF3 uterine cancer 0.0092717 0.8671069 0.00927 ** NA
ZNF207 uterine cancer 0.0093098 0.8671069 0.00931 ** NA
RASIP1 colorectal cancer 0.0093199 0.8671069 0.00932 ** NA
AP5B1 uterine cancer 0.0093215 0.8671069 0.00932 ** NA
RAD50 leukemia 0.0094090 0.8671069 0.00941 ** NA
KRT17 breast cancer 0.0094667 0.8671069 0.00947 ** NA
SCFD1 colorectal cancer 0.0094882 0.8671069 0.00949 ** NA
DNAH5 colorectal cancer 0.0095928 0.8671069 0.00959 ** NA
INTS4 uterine cancer 0.0096407 0.8671069 0.00964 ** NA
TTF1 leukemia 0.0097690 0.8671069 0.00977 ** NA
LRP4 uterine cancer 0.0099566 0.8671069 0.00996 ** NA
ZNF469 uterine cancer 0.0099581 0.8671069 0.00996 ** NA
IPO4 uterine cancer 0.0099688 0.8671069 0.00997 ** NA
wilcox_lineage_nonsilent_plot <- ggplot(data = crispr_signif_nonsilent_lineage) +
  geom_histogram(aes(x = p, fill = "chartreuse4"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  geom_histogram(aes(x = p.adj, fill = "darkslategray3"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  scale_x_continuous(breaks = seq(0, 1, by = 0.05), labels = seq(0, 1, by = 0.05)) +
  scale_fill_manual(name = "P-values", values = c("chartreuse4" = "chartreuse4", "darkslategray3" = "darkslategray3"), labels = c("Unadjusted", "BH-adjusted")) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = c(0.1, 0.85)) +
  labs(x = "BH-adjusted p-values", y = "Frequency")
wilcox_lineage_nonsilent_plot

4.2.3 Deleterious and missense mutant vs. Other

crispr_signif_delmis_lineage <- compare_means(Score ~ Mutation_Status_DeleteriousMissense, group.by = c("Hugo_Symbol", "group_general_lineage_name"), data = crispr_data_ptmuts, method = "wilcox.test", p.adjust.method = "BH")
crispr_signif_delmis_lineage <- adj_signif(crispr_signif_delmis_lineage)
crispr_signif_delmis_lineage <- crispr_signif_delmis_lineage[order(crispr_signif_delmis_lineage$p),]
saveRDS(crispr_signif_delmis_lineage, "./data_munging/rds/crispr_signif_ptmuts_deleteriousmissense_lineage.rds")

# write.table(crispr_signif_delmis_lineage, file = "~/Desktop/crispr_signif_ptmuts_deleteriousmissense_lineage.csv", quote = FALSE, sep = ",", row.names = FALSE)
crispr_signif_delmis_lineage <- readRDS("./data_munging/rds/crispr_signif_ptmuts_deleteriousmissense_lineage.rds")

knitr::kable(filter(crispr_signif_delmis_lineage, p < 0.01)[, c("Hugo_Symbol", "group_general_lineage_name", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "Wilcoxon test results comparing deleterious and missense mutant vs other cell lines by lineage, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
Wilcoxon test results comparing deleterious and missense mutant vs other cell lines by lineage, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Hugo_Symbol group_general_lineage_name p p.adj p.format p.signif p.signif.adj
KRAS lung cancer 0.0000000 0.0003122 2.5e-09 **** ***
TP53 lung cancer 0.0000092 0.5624195 9.2e-06 **** NA
KRAS ovarian cancer 0.0001488 0.8669363 0.00015 *** NA
TP53 central nervous system cancer 0.0001651 0.8669363 0.00017 *** NA
TP53 ovarian cancer 0.0002344 0.8669363 0.00023 *** NA
NRAS leukemia 0.0005316 0.8669363 0.00053 *** NA
PIK3CA ovarian cancer 0.0008371 0.8669363 0.00084 *** NA
DZANK1 breast cancer 0.0009819 0.8669363 0.00098 *** NA
PIK3CA breast cancer 0.0009842 0.8669363 0.00098 *** NA
KRAS colorectal cancer 0.0010547 0.8669363 0.00105 ** NA
TP53 leukemia 0.0010605 0.8669363 0.00106 ** NA
UNC45B lung cancer 0.0011215 0.8669363 0.00112 ** NA
GOLGA3 uterine cancer 0.0013666 0.8669363 0.00137 ** NA
NRAS multiple myeloma 0.0013756 0.8669363 0.00138 ** NA
GTF3C1 lung cancer 0.0013893 0.8669363 0.00139 ** NA
TICRR uterine cancer 0.0014839 0.8669363 0.00148 ** NA
ARID1A pancreatic cancer 0.0014920 0.8669363 0.00149 ** NA
NRAS skin cancer 0.0016637 0.8669363 0.00166 ** NA
HYOU1 colorectal cancer 0.0018107 0.8669363 0.00181 ** NA
USP34 breast cancer 0.0018820 0.8669363 0.00188 ** NA
MGA uterine cancer 0.0019495 0.8669363 0.00195 ** NA
TNRC6A lung cancer 0.0023744 0.8669363 0.00237 ** NA
SNX29 uterine cancer 0.0026104 0.8669363 0.00261 ** NA
KMT2B breast cancer 0.0026433 0.8669363 0.00264 ** NA
TP53 kidney cancer 0.0027562 0.8669363 0.00276 ** NA
GSPT1 colorectal cancer 0.0028115 0.8669363 0.00281 ** NA
AP1G2 breast cancer 0.0029835 0.8669363 0.00298 ** NA
ZNF264 colorectal cancer 0.0029879 0.8669363 0.00299 ** NA
PTEN ovarian cancer 0.0032141 0.8669363 0.00321 ** NA
KMT2B ovarian cancer 0.0032684 0.8669363 0.00327 ** NA
KIAA0586 colorectal cancer 0.0032838 0.8669363 0.00328 ** NA
ANK2 colorectal cancer 0.0033597 0.8669363 0.00336 ** NA
TGS1 uterine cancer 0.0035864 0.8669363 0.00359 ** NA
COL2A1 leukemia 0.0036054 0.8669363 0.00361 ** NA
HYDIN colorectal cancer 0.0036704 0.8669363 0.00367 ** NA
TP53 colorectal cancer 0.0036994 0.8669363 0.00370 ** NA
TP53 skin cancer 0.0037227 0.8669363 0.00372 ** NA
VHL kidney cancer 0.0037539 0.8669363 0.00375 ** NA
PAPPA uterine cancer 0.0037971 0.8669363 0.00380 ** NA
ZNF292 lung cancer 0.0038193 0.8669363 0.00382 ** NA
PTEN central nervous system cancer 0.0038648 0.8669363 0.00386 ** NA
NEBL uterine cancer 0.0039522 0.8669363 0.00395 ** NA
KRAS stomach cancer 0.0040001 0.8669363 0.00400 ** NA
MTMR3 lung cancer 0.0041192 0.8669363 0.00412 ** NA
TSKS uterine cancer 0.0042550 0.8669363 0.00425 ** NA
ARHGAP12 colorectal cancer 0.0042860 0.8669363 0.00429 ** NA
PAM uterine cancer 0.0043488 0.8669363 0.00435 ** NA
PDE10A uterine cancer 0.0043488 0.8669363 0.00435 ** NA
NCOR2 colorectal cancer 0.0046575 0.8669363 0.00466 ** NA
VAV3 lung cancer 0.0047769 0.8669363 0.00478 ** NA
SYNE1 lung cancer 0.0047800 0.8669363 0.00478 ** NA
NUP88 uterine cancer 0.0048888 0.8669363 0.00489 ** NA
LRRIQ1 central nervous system cancer 0.0049271 0.8669363 0.00493 ** NA
SVOPL lung cancer 0.0049276 0.8669363 0.00493 ** NA
PTEN uterine cancer 0.0049419 0.8669363 0.00494 ** NA
BRAF skin cancer 0.0049885 0.8669363 0.00499 ** NA
AXIN1 ovarian cancer 0.0050354 0.8669363 0.00504 ** NA
TNK2 uterine cancer 0.0050864 0.8669363 0.00509 ** NA
DIP2C kidney cancer 0.0052747 0.8669363 0.00527 ** NA
CTAGE15 lung cancer 0.0053375 0.8669363 0.00534 ** NA
LSP1 colorectal cancer 0.0053452 0.8669363 0.00535 ** NA
CORIN colorectal cancer 0.0054756 0.8669363 0.00548 ** NA
SETD2 kidney cancer 0.0056647 0.8669363 0.00566 ** NA
FNDC7 colorectal cancer 0.0057059 0.8669363 0.00571 ** NA
SKOR1 uterine cancer 0.0057795 0.8669363 0.00578 ** NA
PARD3B uterine cancer 0.0059302 0.8669363 0.00593 ** NA
HTR7 lung cancer 0.0059492 0.8669363 0.00595 ** NA
FNIP1 ovarian cancer 0.0060557 0.8669363 0.00606 ** NA
AP2A2 breast cancer 0.0061570 0.8669363 0.00616 ** NA
ASXL1 colorectal cancer 0.0061648 0.8669363 0.00616 ** NA
CCT8L2 colorectal cancer 0.0061648 0.8669363 0.00616 ** NA
MTMR14 colorectal cancer 0.0061648 0.8669363 0.00616 ** NA
TTC32 uterine cancer 0.0062881 0.8669363 0.00629 ** NA
NR4A1 lung cancer 0.0064934 0.8669363 0.00649 ** NA
SLC22A17 lung cancer 0.0064934 0.8669363 0.00649 ** NA
FAM208B colorectal cancer 0.0065065 0.8669363 0.00651 ** NA
ROCK1 colorectal cancer 0.0066106 0.8669363 0.00661 ** NA
ZNF521 colorectal cancer 0.0066975 0.8669363 0.00670 ** NA
RFWD3 lung cancer 0.0067488 0.8669363 0.00675 ** NA
TYRP1 lung cancer 0.0067488 0.8669363 0.00675 ** NA
ADRBK2 uterine cancer 0.0067676 0.8669363 0.00677 ** NA
DVL2 uterine cancer 0.0067676 0.8669363 0.00677 ** NA
EXOC8 uterine cancer 0.0067676 0.8669363 0.00677 ** NA
ITSN1 colorectal cancer 0.0069403 0.8669363 0.00694 ** NA
ZNF292 colorectal cancer 0.0069403 0.8669363 0.00694 ** NA
MIS18BP1 colorectal cancer 0.0070098 0.8669363 0.00701 ** NA
OR5M8 colorectal cancer 0.0070184 0.8669363 0.00702 ** NA
PIK3CA colorectal cancer 0.0070506 0.8669363 0.00705 ** NA
DDX11 lung cancer 0.0070952 0.8669363 0.00710 ** NA
KMT2C multiple myeloma 0.0071541 0.8669363 0.00715 ** NA
MYH6 uterine cancer 0.0071786 0.8669363 0.00718 ** NA
ARID1B ovarian cancer 0.0072055 0.8669363 0.00721 ** NA
SURF6 uterine cancer 0.0073058 0.8669363 0.00731 ** NA
C1orf86 leukemia 0.0073710 0.8669363 0.00737 ** NA
TEX10 ovarian cancer 0.0075601 0.8669363 0.00756 ** NA
ALPK3 ovarian cancer 0.0076300 0.8669363 0.00763 ** NA
MYOM3 lung cancer 0.0076435 0.8669363 0.00764 ** NA
PNPLA5 colorectal cancer 0.0077740 0.8669363 0.00777 ** NA
PTGFRN lung cancer 0.0078691 0.8669363 0.00787 ** NA
PCDH10 uterine cancer 0.0078925 0.8669363 0.00789 ** NA
ANKRD23 colorectal cancer 0.0078965 0.8669363 0.00790 ** NA
HERC1 skin cancer 0.0079501 0.8669363 0.00795 ** NA
C12orf4 uterine cancer 0.0080215 0.8669363 0.00802 ** NA
EGFLAM lung cancer 0.0081906 0.8669363 0.00819 ** NA
SPRED1 lung cancer 0.0084512 0.8669363 0.00845 ** NA
SBF2 lung cancer 0.0084541 0.8669363 0.00845 ** NA
EFCAB5 ovarian cancer 0.0084624 0.8669363 0.00846 ** NA
CSNK1D uterine cancer 0.0085754 0.8669363 0.00858 ** NA
FAM110A uterine cancer 0.0085754 0.8669363 0.00858 ** NA
MICAL1 uterine cancer 0.0085754 0.8669363 0.00858 ** NA
MORF4L1 uterine cancer 0.0085754 0.8669363 0.00858 ** NA
RIOK2 uterine cancer 0.0085754 0.8669363 0.00858 ** NA
GAA colorectal cancer 0.0085960 0.8669363 0.00860 ** NA
KIAA1107 colorectal cancer 0.0085960 0.8669363 0.00860 ** NA
ZNF536 skin cancer 0.0086987 0.8669363 0.00870 ** NA
RPS6KA2 uterine cancer 0.0087779 0.8669363 0.00878 ** NA
SOGA1 lung cancer 0.0088239 0.8669363 0.00882 ** NA
TPR lung cancer 0.0088831 0.8669363 0.00888 ** NA
SHANK1 central nervous system cancer 0.0088878 0.8669363 0.00889 ** NA
TECRL lung cancer 0.0089331 0.8669363 0.00893 ** NA
SVEP1 skin cancer 0.0090238 0.8669363 0.00902 ** NA
DPYSL5 uterine cancer 0.0091517 0.8669363 0.00915 ** NA
FAM189A1 uterine cancer 0.0091517 0.8669363 0.00915 ** NA
IRX6 uterine cancer 0.0091517 0.8669363 0.00915 ** NA
XKR9 uterine cancer 0.0091517 0.8669363 0.00915 ** NA
KIAA0922 lung cancer 0.0091532 0.8669363 0.00915 ** NA
TTF1 lung cancer 0.0092125 0.8669363 0.00921 ** NA
TBC1D10B uterine cancer 0.0092568 0.8669363 0.00926 ** NA
DNAJC8 uterine cancer 0.0092717 0.8669363 0.00927 ** NA
PKDCC uterine cancer 0.0092717 0.8669363 0.00927 ** NA
TCF3 uterine cancer 0.0092717 0.8669363 0.00927 ** NA
ZNF207 uterine cancer 0.0093098 0.8669363 0.00931 ** NA
RASIP1 colorectal cancer 0.0093199 0.8669363 0.00932 ** NA
AP5B1 uterine cancer 0.0093215 0.8669363 0.00932 ** NA
RAD50 leukemia 0.0094090 0.8669363 0.00941 ** NA
KRT17 breast cancer 0.0094667 0.8669363 0.00947 ** NA
SCFD1 colorectal cancer 0.0094882 0.8669363 0.00949 ** NA
DNAH5 colorectal cancer 0.0095928 0.8669363 0.00959 ** NA
INTS4 uterine cancer 0.0096407 0.8669363 0.00964 ** NA
TTF1 leukemia 0.0097690 0.8669363 0.00977 ** NA
LRP4 uterine cancer 0.0099566 0.8669363 0.00996 ** NA
ZNF469 uterine cancer 0.0099581 0.8669363 0.00996 ** NA
IPO4 uterine cancer 0.0099688 0.8669363 0.00997 ** NA
wilcox_gene_delmis_lineage_plot <- ggplot(data = crispr_signif_delmis_lineage) +
  geom_histogram(aes(x = p, fill = "chartreuse4"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  geom_histogram(aes(x = p.adj, fill = "darkslategray3"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  scale_x_continuous(breaks = seq(0, 1, by = 0.05), labels = seq(0, 1, by = 0.05)) +
  scale_fill_manual(name = "P-values", values = c("chartreuse4" = "chartreuse4", "darkslategray3" = "darkslategray3"), labels = c("Unadjusted", "BH-adjusted")) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = c(0.1, 0.85)) +
  labs(x = "BH-adjusted p-values", y = "Frequency")
wilcox_gene_delmis_lineage_plot

5 Wilcoxon tests: All mutations


5.1 Grouped by gene

5.1.1 Deleterious vs. Other

crispr_signif_allmut_del <- compare_means(Score ~ Mutation_Status_Deleterious, group.by = c("Hugo_Symbol"), data = crispr_data, method = "wilcox.test", p.adjust.method = "BH")
crispr_signif_allmut_del <- adj_signif(crispr_signif_allmut_del)
crispr_signif_allmut_del <- crispr_signif_allmut_del[order(crispr_signif_allmut_del$p),]
saveRDS(crispr_signif_allmut_del, "./data_munging/rds/crispr_signif_allmut_deleterious_gene.rds")

# write.table(crispr_signif_allmut_del, file = "~/Desktop/crispr_signif_allmut_deleterious_gene.csv", quote = FALSE, sep = ",", row.names = FALSE)
crispr_signif_allmut_del <- readRDS("./data_munging/rds/crispr_signif_allmut_deleterious_gene.rds")

knitr::kable(filter(crispr_signif_allmut_del, p < 0.01)[, c("Hugo_Symbol", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "Wilcoxon test results comparing deleterious mutant vs other cell lines, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
Wilcoxon test results comparing deleterious mutant vs other cell lines, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Hugo_Symbol p p.adj p.format p.signif p.signif.adj
PTEN 0.0000000 0.0000000 2.9e-13 **** ****
TP53 0.0000002 0.0016540 2.5e-07 **** **
ARID1A 0.0000011 0.0047700 1.1e-06 **** **
VHL 0.0000016 0.0053734 1.6e-06 **** **
UTP20 0.0000301 0.0802533 3.0e-05 **** NA
KRT19 0.0000705 0.1566873 7.0e-05 **** NA
ZC3H13 0.0009557 0.7440112 0.00096 *** NA
ARID1B 0.0010200 0.7440112 0.00102 ** NA
ABCD4 0.0012457 0.7440112 0.00125 ** NA
TCERG1 0.0012735 0.7440112 0.00127 ** NA
NPSR1 0.0013941 0.7440112 0.00139 ** NA
KRTAP10-6 0.0015323 0.7440112 0.00153 ** NA
PLXNA1 0.0016291 0.7440112 0.00163 ** NA
LILRB2 0.0016968 0.7440112 0.00170 ** NA
RNF208 0.0019385 0.7440112 0.00194 ** NA
AJAP1 0.0021018 0.7440112 0.00210 ** NA
HAX1 0.0022315 0.7440112 0.00223 ** NA
TIMD4 0.0022527 0.7440112 0.00225 ** NA
PIGR 0.0028924 0.7440112 0.00289 ** NA
TP53BP2 0.0029507 0.7440112 0.00295 ** NA
MSH6 0.0031226 0.7440112 0.00312 ** NA
KLHL17 0.0038240 0.7440112 0.00382 ** NA
TUBGCP5 0.0038993 0.7440112 0.00390 ** NA
FMN1 0.0039763 0.7440112 0.00398 ** NA
AKAP13 0.0040426 0.7440112 0.00404 ** NA
UGT2A3 0.0041569 0.7440112 0.00416 ** NA
BRD4 0.0042237 0.7440112 0.00422 ** NA
RB1 0.0042661 0.7440112 0.00427 ** NA
NIPBL 0.0042726 0.7440112 0.00427 ** NA
ATAD5 0.0045229 0.7440112 0.00452 ** NA
CR1 0.0047742 0.7440112 0.00477 ** NA
PPIL4 0.0047926 0.7440112 0.00479 ** NA
RINT1 0.0048906 0.7440112 0.00489 ** NA
SIN3A 0.0049114 0.7440112 0.00491 ** NA
PIK3R1 0.0052016 0.7440112 0.00520 ** NA
TPR 0.0052404 0.7440112 0.00524 ** NA
DLD 0.0053697 0.7440112 0.00537 ** NA
PTPRR 0.0055220 0.7440112 0.00552 ** NA
CSTF3 0.0055891 0.7440112 0.00559 ** NA
OLFML2B 0.0058315 0.7440112 0.00583 ** NA
GSTM5 0.0058384 0.7440112 0.00584 ** NA
NOC4L 0.0061581 0.7440112 0.00616 ** NA
KIAA1107 0.0061991 0.7440112 0.00620 ** NA
RTN3 0.0062371 0.7440112 0.00624 ** NA
PLOD3 0.0062880 0.7440112 0.00629 ** NA
ZNF439 0.0063457 0.7440112 0.00635 ** NA
DMKN 0.0063484 0.7440112 0.00635 ** NA
EDNRB 0.0063517 0.7440112 0.00635 ** NA
UTP3 0.0065359 0.7440112 0.00654 ** NA
IQCH 0.0067700 0.7440112 0.00677 ** NA
ST7 0.0068047 0.7440112 0.00680 ** NA
ZNF177 0.0068619 0.7440112 0.00686 ** NA
ENAH 0.0069381 0.7440112 0.00694 ** NA
RTF1 0.0070416 0.7440112 0.00704 ** NA
UTS2B 0.0072533 0.7440112 0.00725 ** NA
TRIM13 0.0073672 0.7440112 0.00737 ** NA
KLHL1 0.0073975 0.7440112 0.00740 ** NA
AMER2 0.0076915 0.7440112 0.00769 ** NA
DMWD 0.0077073 0.7440112 0.00771 ** NA
ATG2A 0.0077283 0.7440112 0.00773 ** NA
NPAT 0.0078418 0.7440112 0.00784 ** NA
SMARCB1 0.0081997 0.7440112 0.00820 ** NA
PAQR3 0.0082276 0.7440112 0.00823 ** NA
SF3B2 0.0084940 0.7440112 0.00849 ** NA
NGRN 0.0085338 0.7440112 0.00853 ** NA
SKOR1 0.0085338 0.7440112 0.00853 ** NA
PHLPP1 0.0085991 0.7440112 0.00860 ** NA
ZBTB25 0.0086381 0.7440112 0.00864 ** NA
ZFR 0.0087353 0.7440112 0.00874 ** NA
ZNF124 0.0089578 0.7440112 0.00896 ** NA
TOP1MT 0.0089680 0.7440112 0.00897 ** NA
NUP98 0.0091458 0.7440112 0.00915 ** NA
THEMIS 0.0092069 0.7440112 0.00921 ** NA
ESYT2 0.0092528 0.7440112 0.00925 ** NA
DZANK1 0.0092793 0.7440112 0.00928 ** NA
SRPR 0.0092882 0.7440112 0.00929 ** NA
PAX5 0.0093106 0.7440112 0.00931 ** NA
ST5 0.0094802 0.7440112 0.00948 ** NA
TTC22 0.0094965 0.7440112 0.00950 ** NA
BUB1 0.0095144 0.7440112 0.00951 ** NA
RIMBP3 0.0096462 0.7440112 0.00965 ** NA
wilcox_gene_allmut_plot <- ggplot(data = crispr_signif_allmut_del) +
  geom_histogram(aes(x = p, fill = "chartreuse4"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  geom_histogram(aes(x = p.adj, fill = "darkslategray3"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  scale_x_continuous(breaks = seq(0, 1, by = 0.05), labels = seq(0, 1, by = 0.05)) +
  scale_fill_manual(name = "P-values", values = c("chartreuse4" = "chartreuse4", "darkslategray3" = "darkslategray3"), labels = c("Unadjusted", "BH-adjusted")) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = c(0.1, 0.85)) +
  labs(x = "BH-adjusted p-values", y = "Frequency")
wilcox_gene_allmut_plot

# ggsave(filename = "./plots_18Q3/crispr/pvals_genes.pdf", plot = wilcox_gene_plot, width = 8, height = 4, device = "pdf")

5.1.2 Non-silent mutant vs. Other

crispr_signif_allmut_nonsilent <- compare_means(Score ~ Mutation_Status_Nonsilent, group.by = c("Hugo_Symbol"), data = crispr_data, method = "wilcox.test", p.adjust.method = "BH")
crispr_signif_allmut_nonsilent <- adj_signif(crispr_signif_allmut_nonsilent)
crispr_signif_allmut_nonsilent <- crispr_signif_allmut_nonsilent[order(crispr_signif_allmut_nonsilent$p),]
saveRDS(crispr_signif_allmut_nonsilent, "./data_munging/rds/crispr_signif_allmut_nonsilent_gene.rds")

# write.table(crispr_signif_allmut_nonsilent, file = "~/Desktop/crispr_signif_allmut_nonsilent_gene.csv", quote = FALSE, sep = ",", row.names = FALSE)
crispr_signif_allmut_nonsilent <- readRDS("./data_munging/rds/crispr_signif_allmut_nonsilent_gene.rds")

knitr::kable(filter(crispr_signif_allmut_nonsilent, p < 0.01)[, c("Hugo_Symbol", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "Wilcoxon test results comparing non-silent mutant vs other cell lines, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
Wilcoxon test results comparing non-silent mutant vs other cell lines, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Hugo_Symbol p p.adj p.format p.signif p.signif.adj
TP53 0.0000000 0.0000000 < 2e-16 **** ****
KRAS 0.0000000 0.0000000 < 2e-16 **** ****
NRAS 0.0000000 0.0000000 < 2e-16 **** ****
BRAF 0.0000000 0.0000000 < 2e-16 **** ****
PTEN 0.0000000 0.0000000 < 2e-16 **** ****
PIK3CA 0.0000000 0.0000000 1.0e-13 **** ****
PIK3R1 0.0000001 0.0003379 1.4e-07 **** ***
CTNNB1 0.0000023 0.0050434 2.3e-06 **** **
TCERG1 0.0000055 0.0104819 5.5e-06 ****
ARID1A 0.0000065 0.0111737 6.5e-06 ****
TPR 0.0000510 0.0797535 5.1e-05 **** NA
VHL 0.0000665 0.0952975 6.7e-05 **** NA
FCGBP 0.0000895 0.1184104 9.0e-05 **** NA
ZNF177 0.0001226 0.1505314 0.00012 *** NA
C14orf39 0.0002001 0.2293087 0.00020 *** NA
SLC22A9 0.0002503 0.2677702 0.00025 *** NA
GSTM5 0.0002648 0.2677702 0.00026 *** NA
NIPBL 0.0003016 0.2880836 0.00030 *** NA
PIGW 0.0003638 0.3291939 0.00036 *** NA
TLX2 0.0004787 0.4114775 0.00048 *** NA
ARFGAP1 0.0005157 0.4159808 0.00052 *** NA
ZNF808 0.0005323 0.4159808 0.00053 *** NA
CD320 0.0005816 0.4347206 0.00058 *** NA
ING2 0.0006989 0.4568910 0.00070 *** NA
MAT2B 0.0007184 0.4568910 0.00072 *** NA
SYNPO2L 0.0007699 0.4568910 0.00077 *** NA
UTP20 0.0007718 0.4568910 0.00077 *** NA
HRAS 0.0008408 0.4568910 0.00084 *** NA
TAOK2 0.0008437 0.4568910 0.00084 *** NA
CPSF1 0.0008505 0.4568910 0.00085 *** NA
UTP3 0.0008549 0.4568910 0.00085 *** NA
RFC1 0.0008787 0.4568910 0.00088 *** NA
RNF208 0.0008907 0.4568910 0.00089 *** NA
KHSRP 0.0009036 0.4568910 0.00090 *** NA
KCNIP4 0.0009472 0.4652404 0.00095 *** NA
GOLGA3 0.0010340 0.4841935 0.00103 ** NA
PROM1 0.0010485 0.4841935 0.00105 ** NA
PMPCA 0.0010990 0.4841935 0.00110 ** NA
MDN1 0.0011415 0.4841935 0.00114 ** NA
PCDHA8 0.0012404 0.4841935 0.00124 ** NA
OR13C4 0.0012702 0.4841935 0.00127 ** NA
FASTKD2 0.0012728 0.4841935 0.00127 ** NA
TELO2 0.0012922 0.4841935 0.00129 ** NA
TAS1R2 0.0013163 0.4841935 0.00132 ** NA
ZNF439 0.0013227 0.4841935 0.00132 ** NA
KRT19 0.0013501 0.4841935 0.00135 ** NA
AVEN 0.0013562 0.4841935 0.00136 ** NA
MTMR2 0.0014414 0.4841935 0.00144 ** NA
E2F1 0.0014486 0.4841935 0.00145 ** NA
IQCH 0.0014602 0.4841935 0.00146 ** NA
LPO 0.0014636 0.4841935 0.00146 ** NA
FRMD4B 0.0014758 0.4841935 0.00148 ** NA
PLA2G4F 0.0014997 0.4841935 0.00150 ** NA
DSEL 0.0015209 0.4841935 0.00152 ** NA
HSD3B7 0.0015616 0.4880853 0.00156 ** NA
PCDHB15 0.0016370 0.4937603 0.00164 ** NA
TAAR1 0.0016765 0.4937603 0.00168 ** NA
MYH13 0.0016962 0.4937603 0.00170 ** NA
ACSM2B 0.0017107 0.4937603 0.00171 ** NA
OR8D1 0.0017233 0.4937603 0.00172 ** NA
KIAA1211L 0.0019402 0.5370893 0.00194 ** NA
SENP8 0.0019559 0.5370893 0.00196 ** NA
RUVBL1 0.0019950 0.5370893 0.00200 ** NA
OR52E8 0.0020406 0.5370893 0.00204 ** NA
CNOT1 0.0020488 0.5370893 0.00205 ** NA
PI4K2A 0.0020961 0.5370893 0.00210 ** NA
KPNA6 0.0021334 0.5370893 0.00213 ** NA
ST6GALNAC1 0.0022068 0.5370893 0.00221 ** NA
POU2F1 0.0022102 0.5370893 0.00221 ** NA
BTBD11 0.0022318 0.5370893 0.00223 ** NA
LPCAT4 0.0022875 0.5370893 0.00229 ** NA
LILRB2 0.0023004 0.5370893 0.00230 ** NA
ANKRD31 0.0023475 0.5370893 0.00235 ** NA
OR51T1 0.0023614 0.5370893 0.00236 ** NA
METTL17 0.0023923 0.5370893 0.00239 ** NA
POLG 0.0024034 0.5370893 0.00240 ** NA
CIITA 0.0024057 0.5370893 0.00241 ** NA
ADAM15 0.0024806 0.5467237 0.00248 ** NA
RAB43 0.0025433 0.5500972 0.00254 ** NA
EZH2 0.0025733 0.5500972 0.00257 ** NA
ZDHHC24 0.0025919 0.5500972 0.00259 ** NA
NFE2L2 0.0026889 0.5525281 0.00269 ** NA
SURF6 0.0027361 0.5525281 0.00274 ** NA
KIF11 0.0027512 0.5525281 0.00275 ** NA
SDHD 0.0027619 0.5525281 0.00276 ** NA
GRM7 0.0027641 0.5525281 0.00276 ** NA
NXPH4 0.0028292 0.5590491 0.00283 ** NA
DENND4C 0.0028765 0.5604565 0.00288 ** NA
LRGUK 0.0029016 0.5604565 0.00290 ** NA
SAMM50 0.0030102 0.5719308 0.00301 ** NA
ACTA1 0.0030465 0.5719308 0.00305 ** NA
SPAG17 0.0030697 0.5719308 0.00307 ** NA
SLC26A2 0.0030940 0.5719308 0.00309 ** NA
MAP2K3 0.0032831 0.5896613 0.00328 ** NA
DIS3L2 0.0032847 0.5896613 0.00328 ** NA
PTPRE 0.0032929 0.5896613 0.00329 ** NA
SCG2 0.0034839 0.6121167 0.00348 ** NA
TRIM8 0.0034895 0.6121167 0.00349 ** NA
CAMK2B 0.0035496 0.6128833 0.00355 ** NA
SLC38A7 0.0036411 0.6128833 0.00364 ** NA
MMRN2 0.0036540 0.6128833 0.00365 ** NA
MAML2 0.0037079 0.6128833 0.00371 ** NA
SGCD 0.0037645 0.6128833 0.00376 ** NA
FBXW7 0.0037715 0.6128833 0.00377 ** NA
ATXN2 0.0037776 0.6128833 0.00378 ** NA
VPS13D 0.0037846 0.6128833 0.00378 ** NA
KRT83 0.0038147 0.6128833 0.00381 ** NA
SUSD5 0.0039261 0.6249422 0.00393 ** NA
ATR 0.0040408 0.6288252 0.00404 ** NA
ATXN2L 0.0041364 0.6288252 0.00414 ** NA
CD83 0.0041819 0.6288252 0.00418 ** NA
ZNF135 0.0042047 0.6288252 0.00420 ** NA
CREBBP 0.0042381 0.6288252 0.00424 ** NA
TOP1MT 0.0042412 0.6288252 0.00424 ** NA
EMILIN3 0.0042659 0.6288252 0.00427 ** NA
SPTBN4 0.0042799 0.6288252 0.00428 ** NA
SPHK2 0.0043100 0.6288252 0.00431 ** NA
GNAI2 0.0043171 0.6288252 0.00432 ** NA
PSMB4 0.0043587 0.6288252 0.00436 ** NA
TPP1 0.0046001 0.6288252 0.00460 ** NA
NFE2L1 0.0046446 0.6288252 0.00464 ** NA
PIGP 0.0046922 0.6288252 0.00469 ** NA
PPP1R1C 0.0047398 0.6288252 0.00474 ** NA
CDC37 0.0047810 0.6288252 0.00478 ** NA
TFAP2A 0.0048201 0.6288252 0.00482 ** NA
CD200R1 0.0048862 0.6288252 0.00489 ** NA
CWH43 0.0048913 0.6288252 0.00489 ** NA
FOXRED1 0.0049144 0.6288252 0.00491 ** NA
HCRTR1 0.0049160 0.6288252 0.00492 ** NA
CACNA1I 0.0050408 0.6288252 0.00504 ** NA
RBBP9 0.0050826 0.6288252 0.00508 ** NA
OGFOD3 0.0051338 0.6288252 0.00513 ** NA
AKR1C1 0.0051682 0.6288252 0.00517 ** NA
TBC1D22A 0.0052429 0.6288252 0.00524 ** NA
APOBEC1 0.0052779 0.6288252 0.00528 ** NA
OR56A3 0.0052916 0.6288252 0.00529 ** NA
SLC46A2 0.0053108 0.6288252 0.00531 ** NA
STK17B 0.0053176 0.6288252 0.00532 ** NA
C6orf15 0.0053410 0.6288252 0.00534 ** NA
SMPD1 0.0053513 0.6288252 0.00535 ** NA
FBXW12 0.0053691 0.6288252 0.00537 ** NA
MGA 0.0053870 0.6288252 0.00539 ** NA
RSPH6A 0.0053903 0.6288252 0.00539 ** NA
EEF2 0.0054341 0.6288252 0.00543 ** NA
CD5L 0.0054356 0.6288252 0.00544 ** NA
AKR1B15 0.0054791 0.6288252 0.00548 ** NA
THADA 0.0055256 0.6288252 0.00553 ** NA
MYOC 0.0055382 0.6288252 0.00554 ** NA
SLC12A4 0.0056227 0.6288252 0.00562 ** NA
DNAJC5B 0.0057016 0.6288252 0.00570 ** NA
TAS2R60 0.0057034 0.6288252 0.00570 ** NA
IBA57 0.0057420 0.6288252 0.00574 ** NA
HDGFRP2 0.0057596 0.6288252 0.00576 ** NA
MRPS34 0.0057928 0.6288252 0.00579 ** NA
AP5B1 0.0057997 0.6288252 0.00580 ** NA
KIF3B 0.0058153 0.6288252 0.00582 ** NA
SSH2 0.0058405 0.6288252 0.00584 ** NA
MBD3L2 0.0058844 0.6288252 0.00588 ** NA
COPS2 0.0059315 0.6288252 0.00593 ** NA
WDR75 0.0059969 0.6288252 0.00600 ** NA
FRYL 0.0060235 0.6288252 0.00602 ** NA
ZNF438 0.0060372 0.6288252 0.00604 ** NA
ELK3 0.0061135 0.6288252 0.00611 ** NA
MUS81 0.0061273 0.6288252 0.00613 ** NA
PRRC1 0.0061280 0.6288252 0.00613 ** NA
CLEC9A 0.0062087 0.6288252 0.00621 ** NA
LMX1A 0.0062097 0.6288252 0.00621 ** NA
SFXN3 0.0062354 0.6288252 0.00624 ** NA
FAM181B 0.0062849 0.6288252 0.00628 ** NA
SMARCB1 0.0062993 0.6288252 0.00630 ** NA
ACBD6 0.0063036 0.6288252 0.00630 ** NA
SUPT7L 0.0063204 0.6288252 0.00632 ** NA
CYP19A1 0.0063281 0.6288252 0.00633 ** NA
FBXL20 0.0064106 0.6333572 0.00641 ** NA
OR4N2 0.0065455 0.6387591 0.00655 ** NA
MIIP 0.0066145 0.6387591 0.00661 ** NA
FCN3 0.0066248 0.6387591 0.00662 ** NA
ZNF107 0.0066752 0.6387591 0.00668 ** NA
MLST8 0.0067360 0.6387591 0.00674 ** NA
TSPAN13 0.0067540 0.6387591 0.00675 ** NA
DPP7 0.0067839 0.6387591 0.00678 ** NA
KDM5B 0.0068837 0.6387591 0.00688 ** NA
IFFO2 0.0068857 0.6387591 0.00689 ** NA
COG3 0.0069083 0.6387591 0.00691 ** NA
DHX35 0.0069214 0.6387591 0.00692 ** NA
PPM1A 0.0069353 0.6387591 0.00694 ** NA
MSGN1 0.0069483 0.6387591 0.00695 ** NA
ANKRD32 0.0071033 0.6427900 0.00710 ** NA
TLN1 0.0071319 0.6427900 0.00713 ** NA
POC1A 0.0071368 0.6427900 0.00714 ** NA
CLDN5 0.0071709 0.6427900 0.00717 ** NA
PRKCZ 0.0072554 0.6427900 0.00726 ** NA
ACTL8 0.0072969 0.6427900 0.00730 ** NA
KRTAP10-6 0.0072992 0.6427900 0.00730 ** NA
SMCR8 0.0073359 0.6427900 0.00734 ** NA
OLFML2B 0.0073612 0.6427900 0.00736 ** NA
HSPB2 0.0073660 0.6427900 0.00737 ** NA
INTS12 0.0075473 0.6534174 0.00755 ** NA
NAA15 0.0075638 0.6534174 0.00756 ** NA
ARSB 0.0076069 0.6538525 0.00761 ** NA
VPS37B 0.0076704 0.6545014 0.00767 ** NA
CCDC159 0.0077369 0.6545014 0.00774 ** NA
SSH1 0.0077665 0.6545014 0.00777 ** NA
NCR3LG1 0.0078580 0.6545014 0.00786 ** NA
ACYP2 0.0079841 0.6545014 0.00798 ** NA
XPO5 0.0080510 0.6545014 0.00805 ** NA
GTF3C1 0.0080700 0.6545014 0.00807 ** NA
OR4S2 0.0081035 0.6545014 0.00810 ** NA
BTAF1 0.0081559 0.6545014 0.00816 ** NA
TACSTD2 0.0081791 0.6545014 0.00818 ** NA
MED30 0.0082419 0.6545014 0.00824 ** NA
ADAM32 0.0082522 0.6545014 0.00825 ** NA
LY9 0.0083045 0.6545014 0.00830 ** NA
H2AFY2 0.0083475 0.6545014 0.00835 ** NA
MRPL13 0.0083762 0.6545014 0.00838 ** NA
PAQR3 0.0084147 0.6545014 0.00841 ** NA
GHITM 0.0084474 0.6545014 0.00845 ** NA
SGK2 0.0084586 0.6545014 0.00846 ** NA
ADO 0.0085378 0.6545014 0.00854 ** NA
ZNF17 0.0085562 0.6545014 0.00856 ** NA
MFAP4 0.0085654 0.6545014 0.00857 ** NA
CD163 0.0085663 0.6545014 0.00857 ** NA
COL7A1 0.0085733 0.6545014 0.00857 ** NA
CD80 0.0085983 0.6545014 0.00860 ** NA
ARMC7 0.0085988 0.6545014 0.00860 ** NA
COL8A1 0.0086043 0.6545014 0.00860 ** NA
KHDC1L 0.0086562 0.6546529 0.00866 ** NA
CHD2 0.0086825 0.6546529 0.00868 ** NA
PLCH2 0.0088368 0.6560317 0.00884 ** NA
OR13C3 0.0088398 0.6560317 0.00884 ** NA
C1orf141 0.0089498 0.6560317 0.00895 ** NA
ARHGEF17 0.0089894 0.6560317 0.00899 ** NA
ARID1B 0.0090009 0.6560317 0.00900 ** NA
COL9A2 0.0090078 0.6560317 0.00901 ** NA
ZC3H4 0.0091088 0.6560317 0.00911 ** NA
SUPT6H 0.0091374 0.6560317 0.00914 ** NA
CCNL1 0.0091597 0.6560317 0.00916 ** NA
TMEM258 0.0091769 0.6560317 0.00918 ** NA
USP32 0.0092096 0.6560317 0.00921 ** NA
DNAJC8 0.0093058 0.6560317 0.00931 ** NA
C20orf26 0.0093466 0.6560317 0.00935 ** NA
KRTAP4-4 0.0093494 0.6560317 0.00935 ** NA
ACHE 0.0093636 0.6560317 0.00936 ** NA
LCTL 0.0093837 0.6560317 0.00938 ** NA
ELMSAN1 0.0093954 0.6560317 0.00940 ** NA
SLC25A4 0.0094344 0.6560317 0.00943 ** NA
F11R 0.0094708 0.6560317 0.00947 ** NA
LY6G6F 0.0095144 0.6560317 0.00951 ** NA
ZNF330 0.0095171 0.6560317 0.00952 ** NA
NECAB1 0.0095689 0.6560317 0.00957 ** NA
GMCL1 0.0096195 0.6560317 0.00962 ** NA
C16orf58 0.0096729 0.6560317 0.00967 ** NA
ZBTB44 0.0096904 0.6560317 0.00969 ** NA
INHBA 0.0097282 0.6560317 0.00973 ** NA
CLEC4C 0.0097545 0.6560317 0.00975 ** NA
GLG1 0.0097693 0.6560317 0.00977 ** NA
RNF31 0.0098119 0.6563315 0.00981 ** NA
COPS3 0.0098996 0.6577057 0.00990 ** NA
NOL6 0.0099717 0.6577057 0.00997 ** NA
WDR77 0.0099778 0.6577057 0.00998 ** NA
wilcox_gene_allmut_nonsilent_plot <- ggplot(data = crispr_signif_allmut_nonsilent) +
  geom_histogram(aes(x = p, fill = "chartreuse4"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  geom_histogram(aes(x = p.adj, fill = "darkslategray3"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  scale_x_continuous(breaks = seq(0, 1, by = 0.05), labels = seq(0, 1, by = 0.05)) +
  scale_fill_manual(name = "P-values", values = c("chartreuse4" = "chartreuse4", "darkslategray3" = "darkslategray3"), labels = c("Unadjusted", "BH-adjusted")) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = c(0.1, 0.85)) +
  labs(x = "BH-adjusted p-values", y = "Frequency")
wilcox_gene_allmut_nonsilent_plot

5.1.3 Deleterious and missense mutant vs. Other

crispr_signif_allmut_delmis <- compare_means(Score ~ Mutation_Status_DeleteriousMissense, group.by = c("Hugo_Symbol"), data = crispr_data, method = "wilcox.test", p.adjust.method = "BH")
crispr_signif_allmut_delmis <- adj_signif(crispr_signif_allmut_delmis)
crispr_signif_allmut_delmis <- crispr_signif_allmut_delmis[order(crispr_signif_allmut_delmis$p),]
saveRDS(crispr_signif_allmut_delmis, "./data_munging/rds/crispr_signif_allmut_deleteriousmissense_gene.rds")

# write.table(crispr_signif_allmut_delmis, file = "~/Desktop/crispr_signif_allmut_deleteriousmissense_gene.csv", quote = FALSE, sep = ",", row.names = FALSE)
crispr_signif_allmut_delmis <- readRDS("./data_munging/rds/crispr_signif_allmut_deleteriousmissense_gene.rds")

knitr::kable(filter(crispr_signif_allmut_delmis, p < 0.01)[, c("Hugo_Symbol", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "Wilcoxon test results comparing deleterious and missense mutant vs other cell lines, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
Wilcoxon test results comparing deleterious and missense mutant vs other cell lines, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Hugo_Symbol p p.adj p.format p.signif p.signif.adj
KRAS 0.0000000 0.0000000 < 2e-16 **** ****
TP53 0.0000000 0.0000000 < 2e-16 **** ****
BRAF 0.0000000 0.0000000 < 2e-16 **** ****
NRAS 0.0000000 0.0000000 < 2e-16 **** ****
PTEN 0.0000000 0.0000000 5.1e-16 **** ****
PIK3CA 0.0000000 0.0000000 9.7e-14 **** ****
CTNNB1 0.0000023 0.0057622 2.3e-06 **** **
TCERG1 0.0000043 0.0092556 4.3e-06 **** **
ARID1A 0.0000077 0.0146617 7.7e-06 ****
TPR 0.0000436 0.0749151 4.4e-05 **** NA
FCGBP 0.0000695 0.1085679 6.9e-05 **** NA
VHL 0.0001029 0.1473663 0.00010 *** NA
ZNF177 0.0001226 0.1620635 0.00012 *** NA
C14orf39 0.0002001 0.2456164 0.00020 *** NA
SLC22A9 0.0002503 0.2844231 0.00025 *** NA
GSTM5 0.0002648 0.2844231 0.00026 *** NA
PIGW 0.0003638 0.3678156 0.00036 *** NA
TLX2 0.0004787 0.4570643 0.00048 *** NA
ARFGAP1 0.0005157 0.4574458 0.00052 *** NA
ZNF808 0.0005323 0.4574458 0.00053 *** NA
CD320 0.0005816 0.4759840 0.00058 *** NA
ING2 0.0006989 0.4783606 0.00070 *** NA
NIPBL 0.0007160 0.4783606 0.00072 *** NA
MAT2B 0.0007184 0.4783606 0.00072 *** NA
SYNPO2L 0.0007699 0.4783606 0.00077 *** NA
UTP20 0.0007718 0.4783606 0.00077 *** NA
HRAS 0.0008408 0.4783606 0.00084 *** NA
CPSF1 0.0008505 0.4783606 0.00085 *** NA
UTP3 0.0008549 0.4783606 0.00085 *** NA
FBXW7 0.0008732 0.4783606 0.00087 *** NA
RFC1 0.0008787 0.4783606 0.00088 *** NA
RNF208 0.0008907 0.4783606 0.00089 *** NA
KCNIP4 0.0009472 0.4932932 0.00095 *** NA
GOLGA3 0.0010340 0.5125264 0.00103 ** NA
PROM1 0.0010485 0.5125264 0.00105 ** NA
PMPCA 0.0010990 0.5125264 0.00110 ** NA
PIK3R1 0.0011525 0.5125264 0.00115 ** NA
PCDHA8 0.0012404 0.5125264 0.00124 ** NA
OR13C4 0.0012702 0.5125264 0.00127 ** NA
MDN1 0.0012746 0.5125264 0.00127 ** NA
TELO2 0.0012922 0.5125264 0.00129 ** NA
TAS1R2 0.0013163 0.5125264 0.00132 ** NA
ZNF439 0.0013227 0.5125264 0.00132 ** NA
KRT19 0.0013501 0.5125264 0.00135 ** NA
MTMR2 0.0014414 0.5125264 0.00144 ** NA
E2F1 0.0014486 0.5125264 0.00145 ** NA
IQCH 0.0014602 0.5125264 0.00146 ** NA
LPO 0.0014636 0.5125264 0.00146 ** NA
FRMD4B 0.0014758 0.5125264 0.00148 ** NA
PLA2G4F 0.0014997 0.5125264 0.00150 ** NA
DSEL 0.0015209 0.5125264 0.00152 ** NA
HSD3B7 0.0015616 0.5160939 0.00156 ** NA
PCDHB15 0.0016370 0.5195965 0.00164 ** NA
TAAR1 0.0016765 0.5195965 0.00168 ** NA
MYH13 0.0016962 0.5195965 0.00170 ** NA
ACSM2B 0.0017107 0.5195965 0.00171 ** NA
OR8D1 0.0017233 0.5195965 0.00172 ** NA
KIAA1211L 0.0019402 0.5430002 0.00194 ** NA
SENP8 0.0019559 0.5430002 0.00196 ** NA
AVEN 0.0019832 0.5430002 0.00198 ** NA
TAOK2 0.0019841 0.5430002 0.00198 ** NA
RUVBL1 0.0019950 0.5430002 0.00200 ** NA
CLEC4C 0.0020406 0.5430002 0.00204 ** NA
OR52E8 0.0020406 0.5430002 0.00204 ** NA
PI4K2A 0.0020961 0.5430002 0.00210 ** NA
KPNA6 0.0021334 0.5430002 0.00213 ** NA
ST6GALNAC1 0.0022068 0.5430002 0.00221 ** NA
POU2F1 0.0022102 0.5430002 0.00221 ** NA
EZH2 0.0022226 0.5430002 0.00222 ** NA
BTBD11 0.0022318 0.5430002 0.00223 ** NA
LPCAT4 0.0022875 0.5430002 0.00229 ** NA
ANKRD31 0.0023475 0.5430002 0.00235 ** NA
OR51T1 0.0023614 0.5430002 0.00236 ** NA
METTL17 0.0023923 0.5430002 0.00239 ** NA
KHSRP 0.0023925 0.5430002 0.00239 ** NA
CIITA 0.0024057 0.5430002 0.00241 ** NA
TUBB2A 0.0024329 0.5430002 0.00243 ** NA
ADAM15 0.0024806 0.5465647 0.00248 ** NA
ZDHHC24 0.0025919 0.5638596 0.00259 ** NA
SURF6 0.0027361 0.5723325 0.00274 ** NA
KIF11 0.0027512 0.5723325 0.00275 ** NA
SDHD 0.0027619 0.5723325 0.00276 ** NA
GRM7 0.0027641 0.5723325 0.00276 ** NA
NXPH4 0.0028292 0.5788467 0.00283 ** NA
SAMM50 0.0030102 0.6042511 0.00301 ** NA
ACTA1 0.0030465 0.6042511 0.00305 ** NA
SPAG17 0.0030697 0.6042511 0.00307 ** NA
SLC26A2 0.0030940 0.6042511 0.00309 ** NA
DENND4C 0.0031465 0.6075857 0.00315 ** NA
ATXN2 0.0032463 0.6085056 0.00325 ** NA
MAP2K3 0.0032831 0.6085056 0.00328 ** NA
DIS3L2 0.0032847 0.6085056 0.00328 ** NA
PTPRE 0.0032929 0.6085056 0.00329 ** NA
CNOT1 0.0034549 0.6246874 0.00345 ** NA
SCG2 0.0034839 0.6246874 0.00348 ** NA
TRIM8 0.0034895 0.6246874 0.00349 ** NA
CAMK2B 0.0035496 0.6288942 0.00355 ** NA
SLC38A7 0.0036411 0.6335632 0.00364 ** NA
MMRN2 0.0036540 0.6335632 0.00365 ** NA
SGCD 0.0037645 0.6335632 0.00376 ** NA
VPS13D 0.0037846 0.6335632 0.00378 ** NA
KRT83 0.0038147 0.6335632 0.00381 ** NA
EEF2 0.0038279 0.6335632 0.00383 ** NA
LILRB2 0.0038340 0.6335632 0.00383 ** NA
SUSD5 0.0039261 0.6376846 0.00393 ** NA
NOL7 0.0039331 0.6376846 0.00393 ** NA
ATR 0.0040408 0.6473519 0.00404 ** NA
ATXN2L 0.0041364 0.6473519 0.00414 ** NA
CD83 0.0041819 0.6473519 0.00418 ** NA
ZNF135 0.0042047 0.6473519 0.00420 ** NA
TOP1MT 0.0042412 0.6473519 0.00424 ** NA
EMILIN3 0.0042659 0.6473519 0.00427 ** NA
SPTBN4 0.0042799 0.6473519 0.00428 ** NA
SPHK2 0.0043100 0.6473519 0.00431 ** NA
PSMB4 0.0043587 0.6473519 0.00436 ** NA
IQGAP3 0.0045407 0.6473519 0.00454 ** NA
TPP1 0.0046001 0.6473519 0.00460 ** NA
NFE2L1 0.0046446 0.6473519 0.00464 ** NA
PIGP 0.0046922 0.6473519 0.00469 ** NA
PPP1R1C 0.0047398 0.6473519 0.00474 ** NA
CDC37 0.0047810 0.6473519 0.00478 ** NA
TFAP2A 0.0048201 0.6473519 0.00482 ** NA
CD200R1 0.0048862 0.6473519 0.00489 ** NA
CWH43 0.0048913 0.6473519 0.00489 ** NA
FOXRED1 0.0049144 0.6473519 0.00491 ** NA
HCRTR1 0.0049160 0.6473519 0.00492 ** NA
RBBP9 0.0050826 0.6473519 0.00508 ** NA
OGFOD3 0.0051338 0.6473519 0.00513 ** NA
AKR1C1 0.0051682 0.6473519 0.00517 ** NA
TBC1D22A 0.0052429 0.6473519 0.00524 ** NA
APOBEC1 0.0052779 0.6473519 0.00528 ** NA
OR56A3 0.0052916 0.6473519 0.00529 ** NA
SLC46A2 0.0053108 0.6473519 0.00531 ** NA
C6orf15 0.0053410 0.6473519 0.00534 ** NA
SMPD1 0.0053513 0.6473519 0.00535 ** NA
FBXW12 0.0053691 0.6473519 0.00537 ** NA
MGA 0.0053870 0.6473519 0.00539 ** NA
RSPH6A 0.0053903 0.6473519 0.00539 ** NA
THADA 0.0055256 0.6473519 0.00553 ** NA
MYOC 0.0055382 0.6473519 0.00554 ** NA
LRGUK 0.0056014 0.6473519 0.00560 ** NA
SLC12A4 0.0056227 0.6473519 0.00562 ** NA
DNAJC5B 0.0057016 0.6473519 0.00570 ** NA
TAS2R60 0.0057034 0.6473519 0.00570 ** NA
IBA57 0.0057420 0.6473519 0.00574 ** NA
HDGFRP2 0.0057596 0.6473519 0.00576 ** NA
MRPS34 0.0057928 0.6473519 0.00579 ** NA
KIF3B 0.0058153 0.6473519 0.00582 ** NA
SSH2 0.0058405 0.6473519 0.00584 ** NA
MBD3L2 0.0058844 0.6473519 0.00588 ** NA
COPS2 0.0059315 0.6473519 0.00593 ** NA
WDR75 0.0059969 0.6473519 0.00600 ** NA
FRYL 0.0060235 0.6473519 0.00602 ** NA
ZNF438 0.0060372 0.6473519 0.00604 ** NA
MMS22L 0.0060571 0.6473519 0.00606 ** NA
ELK3 0.0061135 0.6473519 0.00611 ** NA
MUS81 0.0061273 0.6473519 0.00613 ** NA
PRRC1 0.0061280 0.6473519 0.00613 ** NA
CD5L 0.0061987 0.6473519 0.00620 ** NA
CLEC9A 0.0062087 0.6473519 0.00621 ** NA
LMX1A 0.0062097 0.6473519 0.00621 ** NA
SFXN3 0.0062354 0.6473519 0.00624 ** NA
RAB43 0.0062666 0.6473519 0.00627 ** NA
FAM181B 0.0062849 0.6473519 0.00628 ** NA
SMARCB1 0.0062993 0.6473519 0.00630 ** NA
ACBD6 0.0063036 0.6473519 0.00630 ** NA
SUPT7L 0.0063204 0.6473519 0.00632 ** NA
CYP19A1 0.0063281 0.6473519 0.00633 ** NA
FBXL20 0.0064106 0.6511943 0.00641 ** NA
CEP76 0.0064716 0.6511943 0.00647 ** NA
DPP7 0.0064794 0.6511943 0.00648 ** NA
OR4N2 0.0065455 0.6540139 0.00655 ** NA
MIIP 0.0066145 0.6543288 0.00661 ** NA
FCN3 0.0066248 0.6543288 0.00662 ** NA
ZNF107 0.0066752 0.6555409 0.00668 ** NA
TSPAN13 0.0067540 0.6595153 0.00675 ** NA
KDM5B 0.0068837 0.6597415 0.00688 ** NA
IFFO2 0.0068857 0.6597415 0.00689 ** NA
COG3 0.0069083 0.6597415 0.00691 ** NA
DHX35 0.0069214 0.6597415 0.00692 ** NA
MSGN1 0.0069483 0.6597415 0.00695 ** NA
ANKRD32 0.0071033 0.6599783 0.00710 ** NA
RB1 0.0071617 0.6599783 0.00716 ** NA
CLDN5 0.0071709 0.6599783 0.00717 ** NA
PRKCZ 0.0072554 0.6599783 0.00726 ** NA
ARID1B 0.0072590 0.6599783 0.00726 ** NA
ACTL8 0.0072969 0.6599783 0.00730 ** NA
KRTAP10-6 0.0072992 0.6599783 0.00730 ** NA
EML1 0.0073078 0.6599783 0.00731 ** NA
SMCR8 0.0073359 0.6599783 0.00734 ** NA
OLFML2B 0.0073612 0.6599783 0.00736 ** NA
CREBBP 0.0073732 0.6599783 0.00737 ** NA
F11R 0.0075268 0.6631134 0.00753 ** NA
INTS12 0.0075473 0.6631134 0.00755 ** NA
NAA15 0.0075638 0.6631134 0.00756 ** NA
ARSB 0.0076069 0.6631134 0.00761 ** NA
VPS37B 0.0076704 0.6631134 0.00767 ** NA
CCDC159 0.0077369 0.6631134 0.00774 ** NA
SSH1 0.0077665 0.6631134 0.00777 ** NA
NCR3LG1 0.0078580 0.6631134 0.00786 ** NA
PLOD1 0.0078951 0.6631134 0.00790 ** NA
ACYP2 0.0079841 0.6631134 0.00798 ** NA
XPO5 0.0080510 0.6631134 0.00805 ** NA
AP5B1 0.0080810 0.6631134 0.00808 ** NA
OR4S2 0.0081035 0.6631134 0.00810 ** NA
BTAF1 0.0081559 0.6631134 0.00816 ** NA
TACSTD2 0.0081791 0.6631134 0.00818 ** NA
MED30 0.0082419 0.6631134 0.00824 ** NA
ADAM32 0.0082522 0.6631134 0.00825 ** NA
LY9 0.0083045 0.6631134 0.00830 ** NA
H2AFY2 0.0083475 0.6631134 0.00835 ** NA
MRPL13 0.0083762 0.6631134 0.00838 ** NA
PAQR3 0.0084147 0.6631134 0.00841 ** NA
YLPM1 0.0084185 0.6631134 0.00842 ** NA
GHITM 0.0084474 0.6631134 0.00845 ** NA
SGK2 0.0084586 0.6631134 0.00846 ** NA
ADO 0.0085378 0.6631134 0.00854 ** NA
ZNF17 0.0085562 0.6631134 0.00856 ** NA
MFAP4 0.0085654 0.6631134 0.00857 ** NA
CD163 0.0085663 0.6631134 0.00857 ** NA
COL7A1 0.0085733 0.6631134 0.00857 ** NA
ARMC7 0.0085988 0.6631134 0.00860 ** NA
COL8A1 0.0086043 0.6631134 0.00860 ** NA
KHDC1L 0.0086562 0.6641352 0.00866 ** NA
PLCH2 0.0088368 0.6679256 0.00884 ** NA
OR13C3 0.0088398 0.6679256 0.00884 ** NA
C1orf141 0.0089498 0.6679256 0.00895 ** NA
ARHGEF17 0.0089894 0.6679256 0.00899 ** NA
COL9A2 0.0090078 0.6679256 0.00901 ** NA
H3F3A 0.0090755 0.6679256 0.00908 ** NA
ZC3H4 0.0091088 0.6679256 0.00911 ** NA
SUPT6H 0.0091374 0.6679256 0.00914 ** NA
CCNL1 0.0091597 0.6679256 0.00916 ** NA
MAML2 0.0091672 0.6679256 0.00917 ** NA
TMEM258 0.0091769 0.6679256 0.00918 ** NA
USP32 0.0092096 0.6679256 0.00921 ** NA
DNAJC8 0.0093058 0.6679256 0.00931 ** NA
C20orf26 0.0093466 0.6679256 0.00935 ** NA
KRTAP4-4 0.0093494 0.6679256 0.00935 ** NA
ACHE 0.0093636 0.6679256 0.00936 ** NA
LCTL 0.0093837 0.6679256 0.00938 ** NA
SLC25A4 0.0094344 0.6679256 0.00943 ** NA
LY6G6F 0.0095144 0.6679256 0.00951 ** NA
ZNF330 0.0095171 0.6679256 0.00952 ** NA
NECAB1 0.0095689 0.6679256 0.00957 ** NA
PPM1A 0.0095782 0.6679256 0.00958 ** NA
GMCL1 0.0096195 0.6679256 0.00962 ** NA
C16orf58 0.0096729 0.6679256 0.00967 ** NA
ZBTB44 0.0096904 0.6679256 0.00969 ** NA
INHBA 0.0097282 0.6679256 0.00973 ** NA
GLG1 0.0097693 0.6679256 0.00977 ** NA
RNF31 0.0098119 0.6679256 0.00981 ** NA
COPS3 0.0098996 0.6679256 0.00990 ** NA
NOL6 0.0099717 0.6679256 0.00997 ** NA
WDR77 0.0099778 0.6679256 0.00998 ** NA
wilcox_gene_allmut_delmis_plot <- ggplot(data = crispr_signif_allmut_delmis) +
  geom_histogram(aes(x = p, fill = "chartreuse4"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  geom_histogram(aes(x = p.adj, fill = "darkslategray3"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  scale_x_continuous(breaks = seq(0, 1, by = 0.05), labels = seq(0, 1, by = 0.05)) +
  scale_fill_manual(name = "P-values", values = c("chartreuse4" = "chartreuse4", "darkslategray3" = "darkslategray3"), labels = c("Unadjusted", "BH-adjusted")) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = c(0.1, 0.85)) +
  labs(x = "BH-adjusted p-values", y = "Frequency")
wilcox_gene_allmut_delmis_plot

5.2 Grouped by gene and lineage

5.2.1 Deleterious vs. Other

crispr_signif_allmut_del_lineage <- compare_means(Score ~ Mutation_Status_Deleterious, group.by = c("Hugo_Symbol", "group_general_lineage_name"), data = crispr_data, method = "wilcox.test", p.adjust.method = "BH")
crispr_signif_allmut_del_lineage <- adj_signif(crispr_signif_allmut_del_lineage)
crispr_signif_allmut_del_lineage <- crispr_signif_allmut_del_lineage[order(crispr_signif_allmut_del_lineage$p),]
saveRDS(crispr_signif_allmut_del_lineage, "./data_munging/rds/crispr_signif_allmut_deleterious_lineage.rds")

# write.table(crispr_signif_allmut_del_lineage, file = "~/Desktop/crispr_signif_allmut_deleterious_lineage.csv", quote = FALSE, sep = ",", row.names = FALSE)
crispr_signif_allmut_del_lineage <- readRDS("./data_munging/rds/crispr_signif_allmut_deleterious_lineage.rds")

knitr::kable(filter(crispr_signif_allmut_del_lineage, p < 0.01)[, c("Hugo_Symbol", "group_general_lineage_name", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "Wilcoxon test results comparing deleterious mutant vs other cell lines by lineage, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
Wilcoxon test results comparing deleterious mutant vs other cell lines by lineage, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Hugo_Symbol group_general_lineage_name p p.adj p.format p.signif p.signif.adj
PTEN central nervous system cancer 0.0006658 0.8331843 0.00067 *** NA
TP53 lung cancer 0.0006718 0.8331843 0.00067 *** NA
ZNF141 central nervous system cancer 0.0031757 0.8331843 0.00318 ** NA
DCAF8 lung cancer 0.0033009 0.8331843 0.00330 ** NA
PTEN ovarian cancer 0.0048168 0.8331843 0.00482 ** NA
ARID1B ovarian cancer 0.0054869 0.8331843 0.00549 ** NA
IFT122 lung cancer 0.0055037 0.8331843 0.00550 ** NA
SETD2 kidney cancer 0.0056647 0.8331843 0.00566 ** NA
KRT17 breast cancer 0.0063661 0.8331843 0.00637 ** NA
DVL2 uterine cancer 0.0067676 0.8331843 0.00677 ** NA
ARID1A pancreatic cancer 0.0076726 0.8331843 0.00767 ** NA
MCPH1 lung cancer 0.0077467 0.8331843 0.00775 ** NA
COX4I1 lung cancer 0.0091372 0.8331843 0.00914 ** NA
ZNF343 lung cancer 0.0098486 0.8331843 0.00985 ** NA
wilcox_lineage_allmut_plot <- ggplot(data = crispr_signif_allmut_del_lineage) +
  geom_histogram(aes(x = p, fill = "chartreuse4"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  geom_histogram(aes(x = p.adj, fill = "darkslategray3"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  scale_x_continuous(breaks = seq(0, 1, by = 0.05), labels = seq(0, 1, by = 0.05)) +
  scale_fill_manual(name = "P-values", values = c("chartreuse4" = "chartreuse4", "darkslategray3" = "darkslategray3"), labels = c("Unadjusted", "BH-adjusted")) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = c(0.1, 0.85)) +
  labs(x = "BH-adjusted p-values", y = "Frequency")
wilcox_lineage_allmut_plot

5.2.2 Non-silent mutant vs. Other

crispr_signif_allmut_nonsilent_lineage <- compare_means(Score ~ Mutation_Status_Nonsilent, group.by = c("Hugo_Symbol", "group_general_lineage_name"), data = crispr_data, method = "wilcox.test", p.adjust.method = "BH")
crispr_signif_allmut_nonsilent_lineage <- adj_signif(crispr_signif_allmut_nonsilent_lineage)
crispr_signif_allmut_nonsilent_lineage <- crispr_signif_allmut_nonsilent_lineage[order(crispr_signif_allmut_nonsilent_lineage$p),]
saveRDS(crispr_signif_allmut_nonsilent_lineage, "./data_munging/rds/crispr_signif_allmut_nonsilent_lineage.rds")

# write.table(crispr_signif_allmut_nonsilent_lineage, file = "~/Desktop/crispr_signif_allmut_nonsilent_lineage.csv", quote = FALSE, sep = ",", row.names = FALSE)
crispr_signif_allmut_nonsilent_lineage <- readRDS("./data_munging/rds/crispr_signif_allmut_nonsilent_lineage.rds")

knitr::kable(filter(crispr_signif_allmut_nonsilent_lineage, p < 0.01)[, c("Hugo_Symbol", "group_general_lineage_name", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "Wilcoxon test results comparing non-silent mutant vs other cell lines by lineage, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
Wilcoxon test results comparing non-silent mutant vs other cell lines by lineage, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Hugo_Symbol group_general_lineage_name p p.adj p.format p.signif p.signif.adj
KRAS lung cancer 0.0000000 0.0000734 5.9e-10 **** ****
TP53 lung cancer 0.0000047 0.2961085 4.7e-06 **** NA
TP53 central nervous system cancer 0.0000302 0.8628420 3.0e-05 **** NA
TP53 ovarian cancer 0.0001395 0.8628420 0.00014 *** NA
KRAS ovarian cancer 0.0001488 0.8628420 0.00015 *** NA
NRAS leukemia 0.0005316 0.8628420 0.00053 *** NA
TP53 leukemia 0.0005388 0.8628420 0.00054 *** NA
NRAS skin cancer 0.0006117 0.8628420 0.00061 *** NA
TP53 skin cancer 0.0008277 0.8628420 0.00083 *** NA
PIK3CA ovarian cancer 0.0008371 0.8628420 0.00084 *** NA
DZANK1 breast cancer 0.0009819 0.8628420 0.00098 *** NA
PIK3CA breast cancer 0.0009842 0.8628420 0.00098 *** NA
KRAS colorectal cancer 0.0010547 0.8628420 0.00105 ** NA
UNC45B lung cancer 0.0011215 0.8628420 0.00112 ** NA
VHL kidney cancer 0.0011846 0.8628420 0.00118 ** NA
KRT17 breast cancer 0.0012972 0.8628420 0.00130 ** NA
PIK3R1 central nervous system cancer 0.0013246 0.8628420 0.00132 ** NA
GOLGA3 uterine cancer 0.0013666 0.8628420 0.00137 ** NA
NRAS multiple myeloma 0.0013756 0.8628420 0.00138 ** NA
GTF3C1 lung cancer 0.0013893 0.8628420 0.00139 ** NA
TICRR uterine cancer 0.0014839 0.8628420 0.00148 ** NA
ARID1A pancreatic cancer 0.0014920 0.8628420 0.00149 ** NA
TP53 kidney cancer 0.0016347 0.8628420 0.00163 ** NA
HYDIN colorectal cancer 0.0017600 0.8628420 0.00176 ** NA
PTEN ovarian cancer 0.0018079 0.8628420 0.00181 ** NA
HYOU1 colorectal cancer 0.0018107 0.8628420 0.00181 ** NA
USP34 breast cancer 0.0018820 0.8628420 0.00188 ** NA
MGA uterine cancer 0.0019495 0.8628420 0.00195 ** NA
SVOPL lung cancer 0.0019973 0.8628420 0.00200 ** NA
LILRB2 leukemia 0.0021670 0.8628420 0.00217 ** NA
PIK3R1 ovarian cancer 0.0021769 0.8628420 0.00218 ** NA
KMT2B stomach cancer 0.0022800 0.8628420 0.00228 ** NA
TNRC6A lung cancer 0.0023744 0.8628420 0.00237 ** NA
COX4I1 lung cancer 0.0023949 0.8628420 0.00239 ** NA
SNX29 uterine cancer 0.0026104 0.8628420 0.00261 ** NA
KIF3B ovarian cancer 0.0026230 0.8628420 0.00262 ** NA
KMT2B breast cancer 0.0026433 0.8628420 0.00264 ** NA
GSPT1 colorectal cancer 0.0028115 0.8628420 0.00281 ** NA
BRAF ovarian cancer 0.0028709 0.8628420 0.00287 ** NA
AP1G2 breast cancer 0.0029835 0.8628420 0.00298 ** NA
ZNF264 colorectal cancer 0.0029879 0.8628420 0.00299 ** NA
ZNF141 central nervous system cancer 0.0031757 0.8628420 0.00318 ** NA
KMT2B ovarian cancer 0.0032684 0.8628420 0.00327 ** NA
KIAA0586 colorectal cancer 0.0032838 0.8628420 0.00328 ** NA
ANK2 colorectal cancer 0.0033597 0.8628420 0.00336 ** NA
RPS19BP1 multiple myeloma 0.0034613 0.8628420 0.00346 ** NA
TGS1 uterine cancer 0.0035864 0.8628420 0.00359 ** NA
COL2A1 leukemia 0.0036054 0.8628420 0.00361 ** NA
SLIT3 leukemia 0.0036054 0.8628420 0.00361 ** NA
TP53 colorectal cancer 0.0036994 0.8628420 0.00370 ** NA
HTR7 lung cancer 0.0037248 0.8628420 0.00372 ** NA
PAPPA uterine cancer 0.0037971 0.8628420 0.00380 ** NA
ZNF292 lung cancer 0.0038193 0.8628420 0.00382 ** NA
PTEN central nervous system cancer 0.0038648 0.8628420 0.00386 ** NA
KRAS stomach cancer 0.0040001 0.8628420 0.00400 ** NA
MTMR3 lung cancer 0.0041192 0.8628420 0.00412 ** NA
TSKS uterine cancer 0.0042550 0.8628420 0.00425 ** NA
ARHGAP12 colorectal cancer 0.0042860 0.8628420 0.00429 ** NA
PAM uterine cancer 0.0043488 0.8628420 0.00435 ** NA
PDE10A uterine cancer 0.0043488 0.8628420 0.00435 ** NA
BRAF skin cancer 0.0043710 0.8628420 0.00437 ** NA
ECT2L central nervous system cancer 0.0044085 0.8628420 0.00441 ** NA
MAML2 uterine cancer 0.0044510 0.8628420 0.00445 ** NA
ARID1B ovarian cancer 0.0046380 0.8628420 0.00464 ** NA
NCOR2 colorectal cancer 0.0046575 0.8628420 0.00466 ** NA
NLRP9 lung cancer 0.0046872 0.8628420 0.00469 ** NA
LRP4 uterine cancer 0.0046963 0.8628420 0.00470 ** NA
VAV3 lung cancer 0.0047769 0.8628420 0.00478 ** NA
NUP88 uterine cancer 0.0048888 0.8628420 0.00489 ** NA
LRRIQ1 central nervous system cancer 0.0049271 0.8628420 0.00493 ** NA
PTEN uterine cancer 0.0050051 0.8628420 0.00501 ** NA
AXIN1 ovarian cancer 0.0050354 0.8628420 0.00504 ** NA
PIK3CA colorectal cancer 0.0050602 0.8628420 0.00506 ** NA
TNK2 uterine cancer 0.0050864 0.8628420 0.00509 ** NA
DIP2C kidney cancer 0.0052747 0.8628420 0.00527 ** NA
CTAGE15 lung cancer 0.0053375 0.8628420 0.00534 ** NA
LSP1 colorectal cancer 0.0053452 0.8628420 0.00535 ** NA
CORIN colorectal cancer 0.0054756 0.8628420 0.00548 ** NA
SETD2 kidney cancer 0.0056647 0.8628420 0.00566 ** NA
FNDC7 colorectal cancer 0.0057059 0.8628420 0.00571 ** NA
SKOR1 uterine cancer 0.0057795 0.8628420 0.00578 ** NA
PARD3B uterine cancer 0.0059302 0.8628420 0.00593 ** NA
FNIP1 ovarian cancer 0.0060557 0.8628420 0.00606 ** NA
AP2A2 breast cancer 0.0061570 0.8628420 0.00616 ** NA
ASXL1 colorectal cancer 0.0061648 0.8628420 0.00616 ** NA
CCT8L2 colorectal cancer 0.0061648 0.8628420 0.00616 ** NA
MTMR14 colorectal cancer 0.0061648 0.8628420 0.00616 ** NA
TTC32 uterine cancer 0.0062881 0.8628420 0.00629 ** NA
NR4A1 lung cancer 0.0064934 0.8628420 0.00649 ** NA
SLC22A17 lung cancer 0.0064934 0.8628420 0.00649 ** NA
FAM208B colorectal cancer 0.0065065 0.8628420 0.00651 ** NA
ROCK1 colorectal cancer 0.0066106 0.8628420 0.00661 ** NA
ZNF521 colorectal cancer 0.0066975 0.8628420 0.00670 ** NA
RFWD3 lung cancer 0.0067488 0.8628420 0.00675 ** NA
TYRP1 lung cancer 0.0067488 0.8628420 0.00675 ** NA
ADRBK2 uterine cancer 0.0067676 0.8628420 0.00677 ** NA
DVL2 uterine cancer 0.0067676 0.8628420 0.00677 ** NA
EXOC8 uterine cancer 0.0067676 0.8628420 0.00677 ** NA
VPS51 pancreatic cancer 0.0067999 0.8628420 0.00680 ** NA
ITSN1 colorectal cancer 0.0069403 0.8628420 0.00694 ** NA
ZNF292 colorectal cancer 0.0069403 0.8628420 0.00694 ** NA
OR5M8 colorectal cancer 0.0070184 0.8628420 0.00702 ** NA
C10orf76 ovarian cancer 0.0070773 0.8628420 0.00708 ** NA
DDX11 lung cancer 0.0070952 0.8628420 0.00710 ** NA
KMT2C multiple myeloma 0.0071541 0.8628420 0.00715 ** NA
MYH6 uterine cancer 0.0071786 0.8628420 0.00718 ** NA
SURF6 uterine cancer 0.0073058 0.8628420 0.00731 ** NA
GLP2R lung cancer 0.0073425 0.8628420 0.00734 ** NA
C1orf86 leukemia 0.0073710 0.8628420 0.00737 ** NA
TEX10 ovarian cancer 0.0075601 0.8628420 0.00756 ** NA
ALPK3 ovarian cancer 0.0076300 0.8628420 0.00763 ** NA
MYOM3 lung cancer 0.0076435 0.8628420 0.00764 ** NA
ALPPL2 ovarian cancer 0.0077480 0.8628420 0.00775 ** NA
PNPLA5 colorectal cancer 0.0077740 0.8628420 0.00777 ** NA
PTPRN2 colorectal cancer 0.0078299 0.8628420 0.00783 ** NA
PTGFRN lung cancer 0.0078691 0.8628420 0.00787 ** NA
PCDH10 uterine cancer 0.0078925 0.8628420 0.00789 ** NA
ANKRD23 colorectal cancer 0.0078965 0.8628420 0.00790 ** NA
RB1 central nervous system cancer 0.0079424 0.8628420 0.00794 ** NA
HERC1 skin cancer 0.0079501 0.8628420 0.00795 ** NA
RANBP2 colorectal cancer 0.0079744 0.8628420 0.00797 ** NA
SCFD1 colorectal cancer 0.0079828 0.8628420 0.00798 ** NA
C12orf4 uterine cancer 0.0080215 0.8628420 0.00802 ** NA
EGFLAM lung cancer 0.0081906 0.8628420 0.00819 ** NA
FAM160B1 ovarian cancer 0.0082030 0.8628420 0.00820 ** NA
SPRED1 lung cancer 0.0084512 0.8628420 0.00845 ** NA
MROH1 pancreatic cancer 0.0084519 0.8628420 0.00845 ** NA
SBF2 lung cancer 0.0084541 0.8628420 0.00845 ** NA
EFCAB5 ovarian cancer 0.0084624 0.8628420 0.00846 ** NA
THSD7A lung cancer 0.0085271 0.8628420 0.00853 ** NA
C6orf211 uterine cancer 0.0085754 0.8628420 0.00858 ** NA
CSNK1D uterine cancer 0.0085754 0.8628420 0.00858 ** NA
FAM110A uterine cancer 0.0085754 0.8628420 0.00858 ** NA
MICAL1 uterine cancer 0.0085754 0.8628420 0.00858 ** NA
MORF4L1 uterine cancer 0.0085754 0.8628420 0.00858 ** NA
RIOK2 uterine cancer 0.0085754 0.8628420 0.00858 ** NA
GAA colorectal cancer 0.0085960 0.8628420 0.00860 ** NA
KIAA1107 colorectal cancer 0.0085960 0.8628420 0.00860 ** NA
ZNF536 skin cancer 0.0086987 0.8628420 0.00870 ** NA
RPS6KA2 uterine cancer 0.0087779 0.8628420 0.00878 ** NA
NF1 peripheral nervous system neoplasm 0.0088143 0.8628420 0.00881 ** NA
TPR lung cancer 0.0088831 0.8628420 0.00888 ** NA
NFE2L2 esophageal cancer 0.0088890 0.8628420 0.00889 ** NA
TECRL lung cancer 0.0089331 0.8628420 0.00893 ** NA
CLEC9A liver cancer 0.0090085 0.8628420 0.00901 ** NA
SVEP1 skin cancer 0.0090238 0.8628420 0.00902 ** NA
DPYSL5 uterine cancer 0.0091517 0.8628420 0.00915 ** NA
FAM189A1 uterine cancer 0.0091517 0.8628420 0.00915 ** NA
HSPB2 uterine cancer 0.0091517 0.8628420 0.00915 ** NA
IRX6 uterine cancer 0.0091517 0.8628420 0.00915 ** NA
XKR9 uterine cancer 0.0091517 0.8628420 0.00915 ** NA
KIAA0922 lung cancer 0.0091532 0.8628420 0.00915 ** NA
STK17B lung cancer 0.0092125 0.8628420 0.00921 ** NA
TTF1 lung cancer 0.0092125 0.8628420 0.00921 ** NA
SYNE1 lung cancer 0.0092324 0.8628420 0.00923 ** NA
TBC1D10B uterine cancer 0.0092568 0.8628420 0.00926 ** NA
DNAJC8 uterine cancer 0.0092717 0.8628420 0.00927 ** NA
PKDCC uterine cancer 0.0092717 0.8628420 0.00927 ** NA
TCF3 uterine cancer 0.0092717 0.8628420 0.00927 ** NA
ZNF207 uterine cancer 0.0093098 0.8628420 0.00931 ** NA
RASIP1 colorectal cancer 0.0093199 0.8628420 0.00932 ** NA
AP5B1 uterine cancer 0.0093215 0.8628420 0.00932 ** NA
RAD50 leukemia 0.0094090 0.8628420 0.00941 ** NA
DNAH5 colorectal cancer 0.0095928 0.8628420 0.00959 ** NA
TJP3 head and neck cancer 0.0096224 0.8628420 0.00962 ** NA
NRP2 colorectal cancer 0.0096226 0.8628420 0.00962 ** NA
INTS4 uterine cancer 0.0096407 0.8628420 0.00964 ** NA
TCHH liver cancer 0.0096613 0.8628420 0.00966 ** NA
TTF1 leukemia 0.0097690 0.8628420 0.00977 ** NA
ZNF343 lung cancer 0.0098486 0.8628420 0.00985 ** NA
ZNF469 uterine cancer 0.0099581 0.8628420 0.00996 ** NA
IPO4 uterine cancer 0.0099688 0.8628420 0.00997 ** NA
wilcox_lineage_allmut_nonsilent_plot <- ggplot(data = crispr_signif_allmut_nonsilent_lineage) +
  geom_histogram(aes(x = p, fill = "chartreuse4"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  geom_histogram(aes(x = p.adj, fill = "darkslategray3"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  scale_x_continuous(breaks = seq(0, 1, by = 0.05), labels = seq(0, 1, by = 0.05)) +
  scale_fill_manual(name = "P-values", values = c("chartreuse4" = "chartreuse4", "darkslategray3" = "darkslategray3"), labels = c("Unadjusted", "BH-adjusted")) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = c(0.1, 0.85)) +
  labs(x = "BH-adjusted p-values", y = "Frequency")
wilcox_lineage_allmut_nonsilent_plot

5.2.3 Deleterious and missense mutant vs. Other

crispr_signif_allmut_delmis_lineage <- compare_means(Score ~ Mutation_Status_DeleteriousMissense, group.by = c("Hugo_Symbol", "group_general_lineage_name"), data = crispr_data, method = "wilcox.test", p.adjust.method = "BH")
crispr_signif_allmut_delmis_lineage <- adj_signif(crispr_signif_allmut_delmis_lineage)
crispr_signif_allmut_delmis_lineage <- crispr_signif_allmut_delmis_lineage[order(crispr_signif_allmut_delmis_lineage$p),]
saveRDS(crispr_signif_allmut_delmis_lineage, "./data_munging/rds/crispr_signif_allmut_deleteriousmissens_lineage.rds")

# write.table(crispr_signif_allmut_delmis_lineage, file = "~/Desktop/crispr_signif_allmut_deleteriousmissense_lineage.csv", quote = FALSE, sep = ",", row.names = FALSE)
crispr_signif_allmut_delmis_lineage <- readRDS("./data_munging/rds/crispr_signif_allmut_deleteriousmissens_lineage.rds")

knitr::kable(filter(crispr_signif_allmut_delmis_lineage, p < 0.01)[, c("Hugo_Symbol", "group_general_lineage_name", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "Wilcoxon test results comparing deleterious vs missense mutant and other cell lines, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")
Wilcoxon test results comparing deleterious vs missense mutant and other cell lines, p < 0.01 (BH-adjusted p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)
Hugo_Symbol group_general_lineage_name p p.adj p.format p.signif p.signif.adj
KRAS lung cancer 0.0000000 0.0000729 5.9e-10 **** ****
TP53 lung cancer 0.0000104 0.6484491 1.0e-05 **** NA
TP53 ovarian cancer 0.0001395 0.8627299 0.00014 *** NA
KRAS ovarian cancer 0.0001488 0.8627299 0.00015 *** NA
TP53 central nervous system cancer 0.0004398 0.8627299 0.00044 *** NA
NRAS leukemia 0.0005316 0.8627299 0.00053 *** NA
TP53 leukemia 0.0005388 0.8627299 0.00054 *** NA
NRAS skin cancer 0.0006117 0.8627299 0.00061 *** NA
TP53 skin cancer 0.0008277 0.8627299 0.00083 *** NA
PIK3CA ovarian cancer 0.0008371 0.8627299 0.00084 *** NA
DZANK1 breast cancer 0.0009819 0.8627299 0.00098 *** NA
PIK3CA breast cancer 0.0009842 0.8627299 0.00098 *** NA
KRAS colorectal cancer 0.0010547 0.8627299 0.00105 ** NA
UNC45B lung cancer 0.0011215 0.8627299 0.00112 ** NA
KRT17 breast cancer 0.0012972 0.8627299 0.00130 ** NA
GOLGA3 uterine cancer 0.0013666 0.8627299 0.00137 ** NA
NRAS multiple myeloma 0.0013756 0.8627299 0.00138 ** NA
GTF3C1 lung cancer 0.0013893 0.8627299 0.00139 ** NA
TICRR uterine cancer 0.0014839 0.8627299 0.00148 ** NA
ARID1A pancreatic cancer 0.0014920 0.8627299 0.00149 ** NA
HYOU1 colorectal cancer 0.0018107 0.8627299 0.00181 ** NA
USP34 breast cancer 0.0018820 0.8627299 0.00188 ** NA
MGA uterine cancer 0.0019495 0.8627299 0.00195 ** NA
SVOPL lung cancer 0.0019973 0.8627299 0.00200 ** NA
KMT2B stomach cancer 0.0022800 0.8627299 0.00228 ** NA
ARID1B ovarian cancer 0.0022924 0.8627299 0.00229 ** NA
TNRC6A lung cancer 0.0023744 0.8627299 0.00237 ** NA
SNX29 uterine cancer 0.0026104 0.8627299 0.00261 ** NA
KIF3B ovarian cancer 0.0026230 0.8627299 0.00262 ** NA
KMT2B breast cancer 0.0026433 0.8627299 0.00264 ** NA
GSPT1 colorectal cancer 0.0028115 0.8627299 0.00281 ** NA
AP1G2 breast cancer 0.0029835 0.8627299 0.00298 ** NA
ZNF264 colorectal cancer 0.0029879 0.8627299 0.00299 ** NA
ZNF141 central nervous system cancer 0.0031757 0.8627299 0.00318 ** NA
KMT2B ovarian cancer 0.0032684 0.8627299 0.00327 ** NA
KIAA0586 colorectal cancer 0.0032838 0.8627299 0.00328 ** NA
ANK2 colorectal cancer 0.0033597 0.8627299 0.00336 ** NA
RPS19BP1 multiple myeloma 0.0034613 0.8627299 0.00346 ** NA
TGS1 uterine cancer 0.0035864 0.8627299 0.00359 ** NA
COL2A1 leukemia 0.0036054 0.8627299 0.00361 ** NA
SLIT3 leukemia 0.0036054 0.8627299 0.00361 ** NA
TP53 colorectal cancer 0.0036994 0.8627299 0.00370 ** NA
HTR7 lung cancer 0.0037248 0.8627299 0.00372 ** NA
PAPPA uterine cancer 0.0037971 0.8627299 0.00380 ** NA
ZNF292 lung cancer 0.0038193 0.8627299 0.00382 ** NA
PTEN central nervous system cancer 0.0038648 0.8627299 0.00386 ** NA
PTEN ovarian cancer 0.0039517 0.8627299 0.00395 ** NA
KRAS stomach cancer 0.0040001 0.8627299 0.00400 ** NA
MTMR3 lung cancer 0.0041192 0.8627299 0.00412 ** NA
TSKS uterine cancer 0.0042550 0.8627299 0.00425 ** NA
PTEN uterine cancer 0.0042857 0.8627299 0.00429 ** NA
ARHGAP12 colorectal cancer 0.0042860 0.8627299 0.00429 ** NA
PAM uterine cancer 0.0043488 0.8627299 0.00435 ** NA
PDE10A uterine cancer 0.0043488 0.8627299 0.00435 ** NA
BRAF skin cancer 0.0043710 0.8627299 0.00437 ** NA
ECT2L central nervous system cancer 0.0044085 0.8627299 0.00441 ** NA
VHL kidney cancer 0.0046189 0.8627299 0.00462 ** NA
NCOR2 colorectal cancer 0.0046575 0.8627299 0.00466 ** NA
NLRP9 lung cancer 0.0046872 0.8627299 0.00469 ** NA
LRP4 uterine cancer 0.0046963 0.8627299 0.00470 ** NA
VAV3 lung cancer 0.0047769 0.8627299 0.00478 ** NA
NUP88 uterine cancer 0.0048888 0.8627299 0.00489 ** NA
LRRIQ1 central nervous system cancer 0.0049271 0.8627299 0.00493 ** NA
AXIN1 ovarian cancer 0.0050354 0.8627299 0.00504 ** NA
TNK2 uterine cancer 0.0050864 0.8627299 0.00509 ** NA
DIP2C kidney cancer 0.0052747 0.8627299 0.00527 ** NA
CTAGE15 lung cancer 0.0053375 0.8627299 0.00534 ** NA
LSP1 colorectal cancer 0.0053452 0.8627299 0.00535 ** NA
CORIN colorectal cancer 0.0054756 0.8627299 0.00548 ** NA
SETD2 kidney cancer 0.0056647 0.8627299 0.00566 ** NA
FNDC7 colorectal cancer 0.0057059 0.8627299 0.00571 ** NA
SKOR1 uterine cancer 0.0057795 0.8627299 0.00578 ** NA
HYDIN colorectal cancer 0.0057929 0.8627299 0.00579 ** NA
PARD3B uterine cancer 0.0059302 0.8627299 0.00593 ** NA
FNIP1 ovarian cancer 0.0060557 0.8627299 0.00606 ** NA
AP2A2 breast cancer 0.0061570 0.8627299 0.00616 ** NA
ASXL1 colorectal cancer 0.0061648 0.8627299 0.00616 ** NA
CCT8L2 colorectal cancer 0.0061648 0.8627299 0.00616 ** NA
MTMR14 colorectal cancer 0.0061648 0.8627299 0.00616 ** NA
TTC32 uterine cancer 0.0062881 0.8627299 0.00629 ** NA
NR4A1 lung cancer 0.0064934 0.8627299 0.00649 ** NA
SLC22A17 lung cancer 0.0064934 0.8627299 0.00649 ** NA
FAM208B colorectal cancer 0.0065065 0.8627299 0.00651 ** NA
ROCK1 colorectal cancer 0.0066106 0.8627299 0.00661 ** NA
ZNF521 colorectal cancer 0.0066975 0.8627299 0.00670 ** NA
RFWD3 lung cancer 0.0067488 0.8627299 0.00675 ** NA
TYRP1 lung cancer 0.0067488 0.8627299 0.00675 ** NA
ADRBK2 uterine cancer 0.0067676 0.8627299 0.00677 ** NA
DVL2 uterine cancer 0.0067676 0.8627299 0.00677 ** NA
EXOC8 uterine cancer 0.0067676 0.8627299 0.00677 ** NA
VPS51 pancreatic cancer 0.0067999 0.8627299 0.00680 ** NA
ITSN1 colorectal cancer 0.0069403 0.8627299 0.00694 ** NA
ZNF292 colorectal cancer 0.0069403 0.8627299 0.00694 ** NA
OR5M8 colorectal cancer 0.0070184 0.8627299 0.00702 ** NA
C10orf76 ovarian cancer 0.0070773 0.8627299 0.00708 ** NA
DDX11 lung cancer 0.0070952 0.8627299 0.00710 ** NA
KMT2C multiple myeloma 0.0071541 0.8627299 0.00715 ** NA
MYH6 uterine cancer 0.0071786 0.8627299 0.00718 ** NA
SURF6 uterine cancer 0.0073058 0.8627299 0.00731 ** NA
PIK3CA colorectal cancer 0.0073288 0.8627299 0.00733 ** NA
GLP2R lung cancer 0.0073425 0.8627299 0.00734 ** NA
C1orf86 leukemia 0.0073710 0.8627299 0.00737 ** NA
TEX10 ovarian cancer 0.0075601 0.8627299 0.00756 ** NA
ALPK3 ovarian cancer 0.0076300 0.8627299 0.00763 ** NA
MYOM3 lung cancer 0.0076435 0.8627299 0.00764 ** NA
ALPPL2 ovarian cancer 0.0077480 0.8627299 0.00775 ** NA
TP53 kidney cancer 0.0077589 0.8627299 0.00776 ** NA
PNPLA5 colorectal cancer 0.0077740 0.8627299 0.00777 ** NA
PTPRN2 colorectal cancer 0.0078299 0.8627299 0.00783 ** NA
PTGFRN lung cancer 0.0078691 0.8627299 0.00787 ** NA
PCDH10 uterine cancer 0.0078925 0.8627299 0.00789 ** NA
ANKRD23 colorectal cancer 0.0078965 0.8627299 0.00790 ** NA
RB1 central nervous system cancer 0.0079424 0.8627299 0.00794 ** NA
HERC1 skin cancer 0.0079501 0.8627299 0.00795 ** NA
SCFD1 colorectal cancer 0.0079828 0.8627299 0.00798 ** NA
C12orf4 uterine cancer 0.0080215 0.8627299 0.00802 ** NA
EGFLAM lung cancer 0.0081906 0.8627299 0.00819 ** NA
SPRED1 lung cancer 0.0084512 0.8627299 0.00845 ** NA
MROH1 pancreatic cancer 0.0084519 0.8627299 0.00845 ** NA
SBF2 lung cancer 0.0084541 0.8627299 0.00845 ** NA
EFCAB5 ovarian cancer 0.0084624 0.8627299 0.00846 ** NA
C6orf211 uterine cancer 0.0085754 0.8627299 0.00858 ** NA
CSNK1D uterine cancer 0.0085754 0.8627299 0.00858 ** NA
FAM110A uterine cancer 0.0085754 0.8627299 0.00858 ** NA
MICAL1 uterine cancer 0.0085754 0.8627299 0.00858 ** NA
MORF4L1 uterine cancer 0.0085754 0.8627299 0.00858 ** NA
RIOK2 uterine cancer 0.0085754 0.8627299 0.00858 ** NA
GAA colorectal cancer 0.0085960 0.8627299 0.00860 ** NA
KIAA1107 colorectal cancer 0.0085960 0.8627299 0.00860 ** NA
ZNF536 skin cancer 0.0086987 0.8627299 0.00870 ** NA
RPS6KA2 uterine cancer 0.0087779 0.8627299 0.00878 ** NA
TPR lung cancer 0.0088831 0.8627299 0.00888 ** NA
SHANK1 central nervous system cancer 0.0088878 0.8627299 0.00889 ** NA
TECRL lung cancer 0.0089331 0.8627299 0.00893 ** NA
CLEC9A liver cancer 0.0090085 0.8627299 0.00901 ** NA
SVEP1 skin cancer 0.0090238 0.8627299 0.00902 ** NA
COX4I1 lung cancer 0.0091372 0.8627299 0.00914 ** NA
DPYSL5 uterine cancer 0.0091517 0.8627299 0.00915 ** NA
FAM189A1 uterine cancer 0.0091517 0.8627299 0.00915 ** NA
IRX6 uterine cancer 0.0091517 0.8627299 0.00915 ** NA
XKR9 uterine cancer 0.0091517 0.8627299 0.00915 ** NA
KIAA0922 lung cancer 0.0091532 0.8627299 0.00915 ** NA
TTF1 lung cancer 0.0092125 0.8627299 0.00921 ** NA
SYNE1 lung cancer 0.0092324 0.8627299 0.00923 ** NA
TBC1D10B uterine cancer 0.0092568 0.8627299 0.00926 ** NA
DNAJC8 uterine cancer 0.0092717 0.8627299 0.00927 ** NA
PKDCC uterine cancer 0.0092717 0.8627299 0.00927 ** NA
TCF3 uterine cancer 0.0092717 0.8627299 0.00927 ** NA
ZNF207 uterine cancer 0.0093098 0.8627299 0.00931 ** NA
RASIP1 colorectal cancer 0.0093199 0.8627299 0.00932 ** NA
AP5B1 uterine cancer 0.0093215 0.8627299 0.00932 ** NA
RAD50 leukemia 0.0094090 0.8627299 0.00941 ** NA
DNAH5 colorectal cancer 0.0095928 0.8627299 0.00959 ** NA
TJP3 head and neck cancer 0.0096224 0.8627299 0.00962 ** NA
NRP2 colorectal cancer 0.0096226 0.8627299 0.00962 ** NA
INTS4 uterine cancer 0.0096407 0.8627299 0.00964 ** NA
TCHH liver cancer 0.0096613 0.8627299 0.00966 ** NA
TTF1 leukemia 0.0097690 0.8627299 0.00977 ** NA
ZNF343 lung cancer 0.0098486 0.8627299 0.00985 ** NA
LILRB2 leukemia 0.0098632 0.8627299 0.00986 ** NA
ZNF469 uterine cancer 0.0099581 0.8627299 0.00996 ** NA
IPO4 uterine cancer 0.0099688 0.8627299 0.00997 ** NA
wilcox_lineage_allmut_delmis_plot <- ggplot(data = crispr_signif_allmut_delmis_lineage) +
  geom_histogram(aes(x = p, fill = "chartreuse4"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  geom_histogram(aes(x = p.adj, fill = "darkslategray3"), breaks = seq(0, 1, by = 0.025), color = "black", alpha = 0.7) +
  scale_x_continuous(breaks = seq(0, 1, by = 0.05), labels = seq(0, 1, by = 0.05)) +
  scale_fill_manual(name = "P-values", values = c("chartreuse4" = "chartreuse4", "darkslategray3" = "darkslategray3"), labels = c("Unadjusted", "BH-adjusted")) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = c(0.1, 0.85)) +
  labs(x = "BH-adjusted p-values", y = "Frequency")
wilcox_lineage_allmut_delmis_plot

6 Paper outline


6.1 Figure 1C

Mutation status boxplots for genes identified in G2P.

genes_lvlA <- c("EGFR", "KRAS", "BRAF", "NRAS", "ERBB2", "MET", "ALK", "AKT1", "PDGFRA", "RET")
crispr_ptmuts_lvlA <- filter(crispr_data_ptmuts, Hugo_Symbol %in% genes_lvlA)
crispr_ptmuts_lvlA$Hugo_Symbol <- ordered(crispr_ptmuts_lvlA$Hugo_Symbol, levels = genes_lvlA)
crispr_ptmuts_lvlA_color <- as.character(crispr_ptmuts_lvlA$Color_DeleteriousMissense)
names(crispr_ptmuts_lvlA_color) <- crispr_ptmuts_lvlA$Mutation_Status_DeleteriousMissense

crispr_ptmuts_lvlA_plot <- ggplot(data = crispr_ptmuts_lvlA, aes(x = Hugo_Symbol, y = Score)) +
  geom_hline(yintercept = 0, lty = 2, color = "darkgray") +
  geom_violin(mapping = aes(fill = Mutation_Status_DeleteriousMissense), alpha = 0.7, position = position_dodge(0.85)) +
  geom_boxplot(mapping = aes(color = Mutation_Status_DeleteriousMissense), position = position_dodge(0.85), width = 0.1) +
  scale_fill_manual(values = crispr_ptmuts_lvlA_color) +
  scale_color_manual(values = c("black", "black")) +
  guides(color = FALSE) +
  theme(legend.direction = "horizontal", legend.justification = c(1, 0), legend.position = c(1, 0), legend.box.margin = margin(c(10, 10, 10, 10))) +
  labs(fill = "Mutation Status", y = "CERES Score", x = "Gene")
crispr_ptmuts_lvlA_plot

# ggsave("./plots_18Q3/manuscript/crispr_ptmuts_lvlA_plot.png", crispr_ptmuts_lvlA_plot, device = "png", width = 12, height = 5, units = "in")

crispr_ptmuts_lvlA_plot_faceted <- ggplot(data = crispr_ptmuts_lvlA, aes(x = Mutation_Status_DeleteriousMissense, y = Score)) +
  facet_wrap(~ Hugo_Symbol, ncol = length(unique(crispr_ptmuts_lvlA$Hugo_Symbol))) +
  geom_hline(yintercept = 0, lty = 2, color = "darkgray") +
  geom_violin(mapping = aes(fill = Mutation_Status_DeleteriousMissense), alpha = 0.7, position = position_dodge(0.85)) +
  geom_boxplot(mapping = aes(color = Mutation_Status_DeleteriousMissense), position = position_dodge(0.85), width = 0.1) +
  scale_fill_manual(values = crispr_ptmuts_lvlA_color) +
  scale_color_manual(values = c("black", "black")) +
  guides(color = FALSE) +
  theme(legend.direction = "horizontal", legend.justification = c(0.5, 0), legend.position = c(0.5, 0), legend.box.margin = margin(c(10, 10, 10, 10)), axis.ticks.x = element_blank(), axis.text.x = element_blank(), axis.title.x = element_blank()) +
  labs(fill = "Mutation Status", y = "CERES Score", x = "Mutation Status")
crispr_ptmuts_lvlA_plot_faceted

# ggsave("./plots_18Q3/manuscript/crispr_ptmuts_lvlA_plot_faceted.png", crispr_ptmuts_lvlA_plot_faceted, device = "png", width = 12, height = 5, units = "in")

6.2 Figure 1D

Match specific point mutations.

g2p_indications <- filter(read.delim("./data_munging/data_mutation_associations_appended.csv", sep = "\t", row.names = 1, header = TRUE), Evidence.Level == "A")
maf_g2p_indications <- filter(maf_raw, Genome_Change %in% g2p_indications$MutationName)
crispr_g2p_indications <- merge(maf_g2p_indications, crispr_data_ptmuts, by = c("Hugo_Symbol", "CCLE_Name", "Broad_ID"))
dup_g2p_indications <- filter(crispr_g2p_indications[, c("Hugo_Symbol", "CCLE_Name", "Broad_ID")] %>% group_by(Hugo_Symbol, CCLE_Name, Broad_ID) %>% tally(), n > 1)
crispr_g2p_indications <- merge(crispr_g2p_indications, dup_g2p_indications, by = c("Hugo_Symbol", "Broad_ID"), all.x = TRUE)
crispr_g2p_indications <- filter(crispr_g2p_indications, is.na(n))

crispr_g2p_indications_plot <- ggplot(crispr_g2p_indications, aes(x = Hugo_Symbol, y = Score)) +
  geom_hline(yintercept = 0, lty = 2, color = "darkgray") +
  geom_boxplot(alpha = 0.5, color = "lightgray", outlier.shape = NA) +
  geom_jitter(width = 0.3, mapping = aes(color = group_general_lineage_name), size = 1) +
  theme_light() +
  theme(legend.title = element_blank()) +
  labs(x = "Gene", y = "CERES Score")
crispr_g2p_indications_plot

# ggsave("./plots_18Q3/manuscript/crispr_g2p_indications_plot_test.png", crispr_g2p_indications_plot, width = 12, height = 5, units = "in")

7 Heatmaps


7.1 Data management

The mutation status portion of these heatmaps was made using deleterious and missense mutations as “mutant” and all others as “other.”

Make and save heatmap data matrices:

# Set up heatmap data
chm_scores <- t(crispr)
colnames(chm_scores) <- chm_scores[1, ]
chm_scores <- chm_scores[-1, ]
chm_rows <- rownames(chm_scores)
chm_cols <- colnames(chm_scores)
chm_scores <- apply(chm_scores, 2, as.double)
chm_scores <- apply(chm_scores, 2, function(x) (x - mean(x)) / var(x))
colnames(chm_scores) <- chm_cols
rownames(chm_scores) <- chm_rows
saveRDS(chm_scores, "./data_munging/rds/crispr_heatmap_scores.rds")

# Make full grid for genomic features
crispr_grid_ccls <- unique(crispr_data$Broad_ID)
crispr_grid_genes <- unique(crispr_data$Hugo_Symbol)
crispr_grid <- expand.grid("Broad_ID" = crispr_grid_ccls, "Hugo_Symbol" = crispr_grid_genes)

# Gene expression
ge_filt <- filter(ge_melt, Hugo_Symbol %in% unique(crispr_data$Hugo_Symbol))[, c("Hugo_Symbol", "Broad_ID", "RPKM")]
ge_filt$RPKM <- log2(ge_filt$RPKM + 0.0001)
chm_ge_grid <- merge(crispr_grid, ge_filt, by = c("Broad_ID", "Hugo_Symbol"), all.x = TRUE)
chm_ge <- reshape(chm_ge_grid, idvar = "Hugo_Symbol", timevar = "Broad_ID", direction = "wide")
chm_ge_rows <- chm_ge$Hugo_Symbol
chm_ge$Hugo_Symbol <- NULL
chm_ge_cols <- colnames(chm_ge) %>% gsub("RPKM.", "", .)
chm_ge <- apply(chm_ge, 2, as.double)
chm_ge <- apply(chm_ge, 2, function(x) (x - mean(x, na.rm = TRUE)) / var(x, na.rm = TRUE))
rownames(chm_ge) <- chm_ge_rows
colnames(chm_ge) <- chm_ge_cols
saveRDS(chm_ge, "./data_munging/rds/crispr_heatmap_ge.rds")

# Mutation status
chm_mut_grid <- merge(crispr_grid, maf_df[, c("Broad_ID", "Hugo_Symbol", "Mutation_Status")], by = c("Broad_ID", "Hugo_Symbol"), all.x = TRUE)
chm_mut_grid$Mutation_Status <- ifelse(is.na(chm_mut_grid$Mutation_Status), "Other", chm_mut_grid$Mutation_Status)
chm_mut_grid$Mutation_Status <- ifelse(chm_mut_grid$Mutation_Status == "Other", 0, 1)
chm_mut <- reshape(chm_mut_grid, idvar = "Hugo_Symbol", timevar = "Broad_ID", direction = "wide")
chm_mut_rows <- chm_mut$Hugo_Symbol
chm_mut$Hugo_Symbol <- NULL
chm_mut_cols <- colnames(chm_mut) %>% gsub("Mutation_Status.", "", .)
chm_mut <- matrix(as.numeric(unlist(chm_mut)), nrow = nrow(chm_mut))
rownames(chm_mut) <- chm_mut_rows
colnames(chm_mut) <- chm_mut_cols
saveRDS(chm_mut, "./data_munging/rds/crispr_heatmap_mut.rds")

# Copy number
cn_filt <- filter(cn_melt, Hugo_Symbol %in% unique(crispr_data$Hugo_Symbol))
chm_cn_grid <- merge(crispr_grid, cn_melt, by = c("Broad_ID", "Hugo_Symbol"), all.x = TRUE)
chm_cn <- reshape(chm_cn_grid, idvar = "Hugo_Symbol", timevar = "Broad_ID", direction = "wide")
chm_cn_rows <- chm_cn$Hugo_Symbol
chm_cn$Hugo_Symbol <- NULL
chm_cn_cols <- colnames(chm_cn) %>% gsub("Copy_Number.", "", .)
chm_cn <- apply(chm_cn, 2, as.double)
chm_cn <- apply(chm_cn, 2, function(x) (x - mean(x, na.rm = TRUE)) / var(x, na.rm = TRUE))
rownames(chm_cn) <- chm_cn_rows
colnames(chm_cn) <- chm_cn_cols
saveRDS(chm_cn, "./data_munging/rds/crispr_heatmap_cn.rds")

Load heatmap matrices:

chm_scores <- readRDS("./data_munging/rds/crispr_heatmap_scores.rds")
chm_ge <- readRDS("./data_munging/rds/crispr_heatmap_ge.rds")
chm_mut <- readRDS("./data_munging/rds/crispr_heatmap_mut.rds")
chm_cn <- readRDS("./data_munging/rds/crispr_heatmap_cn.rds")

# Metadata anotation dataframe
chm_annot <- merge(merge(data.frame("Broad_ID" = crispr$Broad_ID), crispr_meta[, c("Broad_ID", "cell_line_SSMD", "cas9_activity", "culture_type", "primary_tissue")], by = "Broad_ID", all.x = TRUE), ccl_info[, c("Broad_ID", "Primary.Disease", "Gender", "Source")], by = "Broad_ID", all.x = TRUE)
rownames(chm_annot) <- chm_annot$Broad_ID
chm_annot$Broad_ID <- NULL
colnames(chm_annot) <- c("Cell_Line_SSMD", "Cas9_Activity", "Culture_Type", "Primary_Tissue", "Primary_Disease", "Gender", "Source")
chm_annot$Cell_Line_SSMD <- as.numeric(chm_annot$Cell_Line_SSMD)
chm_annot$Cas9_Activity <- as.numeric(chm_annot$Cas9_Activity)

7.2 All genes, no omics

chm_annot_list <- HeatmapAnnotation(df = chm_annot, annotation_legend_param = list(
  Cell_Line_SSMD = list(title = "Cell Line SSMD", title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), title_position = "topcenter", legend_height = unit(2, "in")),
  Cas9_Activity = list(title = "Cas9 Activity", title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), title_position = "topcenter", legend_height = unit(2, "in")),
  Culture_Type = list(title = "Culture Type", title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), nrow = 8, title = "Culture Type", title_position = "topleft", grid_height = unit(0.3, "in")),
  Primary_Tissue = list(title = "Primary Tissue", title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), nrow = 8, title = "Primary_Tissue", title_position = "topleft", grid_height = unit(0.3, "in")),
  Primary_Disease = list(title = "Primary Disease", title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), nrow = 8, title = "Primary Disease", title_position = "topleft", grid_height = unit(0.3, "in")),
  Gender = list(title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), grid_height = unit(0.3, "in")),
  Source = list(title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), nrow = 8, title = "Source", title_position = "topleft", grid_height = unit(0.3, "in"))))

chm_all_plot <- Heatmap(chm_scores, name = "CERES Score",
        bottom_annotation = chm_annot_list,
        bottom_annotation_height = unit(5, "in"),
        heatmap_legend_param = list(title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), title_position = "topcenter", legend_height = unit(2, "in")),
        row_title = "Genes", column_title = "Cancer Cell Lines",
        col = colorRamp2(c(min(chm_scores), 0, max(chm_scores)), c("purple4", "white", "seagreen4")),
        na_col = "black",
        row_title_gp = gpar(fontsize = 30, fontface = "bold"),
        row_names_gp = gpar(fontsize = 5),
        column_title_gp = gpar(fontsize = 30, fontface = "bold"),
        column_names_gp = gpar(fontsize = 5),
        row_dend_reorder = TRUE,
        column_dend_reorder = TRUE,
        clustering_distance_rows = "euclidean",
        clustering_distance_columns = "euclidean",
        clustering_method_rows = "complete",
        clustering_method_columns = "complete",
        column_dend_height = unit(2, "in"),
        row_dend_width = unit(4, "in"),
        width = 3)

pdf("./plots_18Q3/crispr_heatmap_euclidean_all.pdf", width = 50, height = 50, paper = "special")
draw(chm_all_plot, heatmap_legend_side = "bottom", annotation_legend_side = "bottom")
seekViewport("annotation_Cell_Line_SSMD")
grid.text("Cell Line SSMD", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Cas9_Activity")
grid.text("Cas9 Activity", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Culture_Type")
grid.text("Culture Type", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Primary_Tissue")
grid.text("Primary Tissue", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Primary_Disease")
grid.text("Primary Disease", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Gender")
grid.text("Gender", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Source")
grid.text("Source", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
dev.off()

png("./plots_18Q3/crispr_heatmap_euclidean_all.png", width = 50, height = 50, units = "in", res = 96)
draw(chm_all_plot, heatmap_legend_side = "bottom", annotation_legend_side = "bottom")
seekViewport("annotation_Cell_Line_SSMD")
grid.text("Cell Line SSMD", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Cas9_Activity")
grid.text("Cas9 Activity", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Culture_Type")
grid.text("Culture Type", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Primary_Tissue")
grid.text("Primary Tissue", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Primary_Disease")
grid.text("Primary Disease", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Gender")
grid.text("Gender", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Source")
grid.text("Source", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
dev.off()

7.3 CGC genes

Code for heatmap of CGC-filtered genes only

7.3.1 Euclidean distance

chm_scores_cgc <- subset(chm_scores, rownames(chm_scores) %in% cgc$Hugo_Symbol)
chm_ge_cgc <- subset(chm_ge, rownames(chm_ge) %in% cgc$Hugo_Symbol)
chm_mut_cgc <- subset(chm_mut, rownames(chm_mut) %in% cgc$Hugo_Symbol)
chm_cn_cgc <- subset(chm_cn, rownames(chm_cn) %in% cgc$Hugo_Symbol)

chm_annot_list <- HeatmapAnnotation(df = chm_annot, annotation_legend_param = list(
  Cell_Line_SSMD = list(title = "Cell Line SSMD", title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), title_position = "topcenter", legend_height = unit(2, "in")),
  Cas9_Activity = list(title = "Cas9 Activity", title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), title_position = "topcenter", legend_height = unit(2, "in")),
  Culture_Type = list(title = "Culture Type", title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), nrow = 8, title = "Culture Type", title_position = "topleft", grid_height = unit(0.3, "in")),
  Primary_Tissue = list(title = "Primary Tissue", title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), nrow = 8, title = "Primary_Tissue", title_position = "topleft", grid_height = unit(0.3, "in")),
  Primary_Disease = list(title = "Primary Disease", title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), nrow = 8, title = "Primary Disease", title_position = "topleft", grid_height = unit(0.3, "in")),
  Gender = list(title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), grid_height = unit(0.3, "in")),
  Source = list(title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), nrow = 8, title = "Source", title_position = "topleft", grid_height = unit(0.3, "in"))))

chm_plot_cgc <- Heatmap(chm_scores_cgc, name = "CERES Score",
        bottom_annotation = chm_annot_list,
        bottom_annotation_height = unit(5, "in"),
        heatmap_legend_param = list(title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), title_position = "topcenter", legend_height = unit(2, "in")),
        row_title = "Genes", column_title = "Cancer Cell Lines",
        col = colorRamp2(c(min(chm_scores_cgc), 0, max(chm_scores_cgc)), c("purple4", "white", "seagreen4")),
        na_col = "black",
        row_title_gp = gpar(fontsize = 30, fontface = "bold"),
        row_names_gp = gpar(fontsize = 5),
        column_title_gp = gpar(fontsize = 30, fontface = "bold"),
        column_names_gp = gpar(fontsize = 5),
        row_dend_reorder = TRUE,
        column_dend_reorder = TRUE,
        clustering_distance_rows = "euclidean",
        clustering_distance_columns = "euclidean",
        clustering_method_rows = "complete",
        clustering_method_columns = "complete",
        column_dend_height = unit(2, "in"),
        row_dend_width = unit(4, "in"),
        width = 3)

chm_plot_cgc_cols <- colnames(chm_scores_cgc)[unlist(column_order(chm_plot_cgc))]
chm_plot_cgc_rows <- rownames(chm_scores_cgc)[unlist(row_order(chm_plot_cgc))]

chm_ge_plot_cgc <- Heatmap(chm_ge_cgc, name = "Gene Expression",
        col = colorRamp2(c(min(chm_ge_cgc[!is.na(chm_ge_cgc)]), 0, max(chm_ge_cgc[!is.na(chm_ge_cgc)])), c("dodgerblue4", "white", "firebrick4")),
        heatmap_legend_param = list(title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), title_position = "topcenter", legend_height = unit(2, "in")),
        na_col = "black",
        cluster_rows = FALSE, cluster_columns = FALSE,
        row_names_gp = gpar(fontsize = 5), show_column_names = FALSE,
        column_title = "Gene Expression",
        column_title_gp = gpar(fontsize = 30, fontface = "bold"),
        column_order = chm_plot_cgc_cols,
        row_order = chm_plot_cgc_rows,
        width = 1)
chm_cn_plot_cgc <- Heatmap(chm_cn_cgc, name = "Copy Number",
        col = colorRamp2(c(min(chm_cn_cgc[!is.na(chm_cn_cgc)]), 0, max(chm_cn_cgc[!is.na(chm_cn_cgc)])), c("white", "thistle2", "mediumorchid4")),
        heatmap_legend_param = list(title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), title_position = "topcenter", legend_height = unit(2, "in")),
        na_col = "black",
        cluster_rows = FALSE, cluster_columns = FALSE,
        row_names_gp = gpar(fontsize = 5), show_column_names = FALSE,
        column_title = "Copy Number",
        column_title_gp = gpar(fontsize = 30, fontface = "bold"),
        column_order = chm_plot_cgc_cols,
        row_order = chm_plot_cgc_rows,
        width = 1)
chm_mut_plot_cgc <- Heatmap(chm_mut_cgc, name = "Mutation Status",
        na_col = "black",
        heatmap_legend_param = list(title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), at = c(0, 1), labels = c("Other", "Mutant"), title_position = "topcenter", grid_height = unit(0.3, "in")),
        col = c("cyan3", "darkorchid"),
        cluster_rows = FALSE, cluster_columns = FALSE,
        row_names_gp = gpar(fontsize = 5), show_column_names = FALSE,
        column_title = "Mutation Status",
        column_title_gp = gpar(fontsize = 30, fontface = "bold"),
        column_order = chm_plot_cgc_cols,
        row_order = chm_plot_cgc_rows,
        width = 1)

chm_all_plot_cgc <- chm_plot_cgc + chm_ge_plot_cgc + chm_cn_plot_cgc + chm_mut_plot_cgc

pdf("./plots_18Q3/crispr_heatmap_euclidean_all_cgc.pdf", width = 50, height = 50, paper = "special")
draw(chm_all_plot_cgc, heatmap_legend_side = "bottom", annotation_legend_side = "bottom")
seekViewport("annotation_Cell_Line_SSMD")
grid.text("Cell Line SSMD", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Cas9_Activity")
grid.text("Cas9 Activity", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Culture_Type")
grid.text("Culture Type", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Primary_Tissue")
grid.text("Primary Tissue", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Primary_Disease")
grid.text("Primary Disease", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Gender")
grid.text("Gender", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Source")
grid.text("Source", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
dev.off()

png("./plots_18Q3/crispr_heatmap_euclidean_all_cgc.png", width = 50, height = 50, units = "in", res = 96)
draw(chm_all_plot_cgc, heatmap_legend_side = "bottom", annotation_legend_side = "bottom")
seekViewport("annotation_Cell_Line_SSMD")
grid.text("Cell Line SSMD", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Cas9_Activity")
grid.text("Cas9 Activity", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Culture_Type")
grid.text("Culture Type", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Primary_Tissue")
grid.text("Primary Tissue", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Primary_Disease")
grid.text("Primary Disease", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Gender")
grid.text("Gender", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Source")
grid.text("Source", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
dev.off()

7.3.2 Pearson correlation

chm_scores_cgc <- subset(chm_scores, rownames(chm_scores) %in% cgc$Hugo_Symbol)
chm_ge_cgc <- subset(chm_ge, rownames(chm_ge) %in% cgc$Hugo_Symbol)
chm_mut_cgc <- subset(chm_mut, rownames(chm_mut) %in% cgc$Hugo_Symbol)
chm_cn_cgc <- subset(chm_cn, rownames(chm_cn) %in% cgc$Hugo_Symbol)

chm_annot_list <- HeatmapAnnotation(df = chm_annot, annotation_legend_param = list(
  Cell_Line_SSMD = list(title = "Cell Line SSMD", title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), title_position = "topcenter", legend_height = unit(2, "in")),
  Cas9_Activity = list(title = "Cas9 Activity", title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), title_position = "topcenter", legend_height = unit(2, "in")),
  Culture_Type = list(title = "Culture Type", title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), nrow = 8, title = "Culture Type", title_position = "topleft", grid_height = unit(0.3, "in")),
  Primary_Tissue = list(title = "Primary Tissue", title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), nrow = 8, title = "Primary_Tissue", title_position = "topleft", grid_height = unit(0.3, "in")),
  Primary_Disease = list(title = "Primary Disease", title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), nrow = 8, title = "Primary Disease", title_position = "topleft", grid_height = unit(0.3, "in")),
  Gender = list(title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), grid_height = unit(0.3, "in")),
  Source = list(title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), nrow = 8, title = "Source", title_position = "topleft", grid_height = unit(0.3, "in"))))

chm_plot_cgc <- Heatmap(chm_scores_cgc, name = "CERES Score",
        bottom_annotation = chm_annot_list,
        bottom_annotation_height = unit(5, "in"),
        heatmap_legend_param = list(title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), title_position = "topcenter", legend_height = unit(2, "in")),
        row_title = "Genes", column_title = "Cancer Cell Lines",
        col = colorRamp2(c(min(chm_scores_cgc), 0, max(chm_scores_cgc)), c("purple4", "white", "seagreen4")),
        na_col = "black",
        row_title_gp = gpar(fontsize = 30, fontface = "bold"),
        row_names_gp = gpar(fontsize = 5),
        column_title_gp = gpar(fontsize = 30, fontface = "bold"),
        column_names_gp = gpar(fontsize = 5),
        row_dend_reorder = TRUE,
        column_dend_reorder = TRUE,
        clustering_distance_rows = "pearson",
        clustering_distance_columns = "pearson",
        clustering_method_rows = "complete",
        clustering_method_columns = "complete",
        column_dend_height = unit(2, "in"),
        row_dend_width = unit(4, "in"),
        width = 3)

chm_plot_cgc_cols <- colnames(chm_scores_cgc)[unlist(column_order(chm_plot_cgc))]
chm_plot_cgc_rows <- rownames(chm_scores_cgc)[unlist(row_order(chm_plot_cgc))]

chm_ge_plot_cgc <- Heatmap(chm_ge_cgc, name = "Gene Expression",
        col = colorRamp2(c(min(chm_ge_cgc[!is.na(chm_ge_cgc)]), 0, max(chm_ge_cgc[!is.na(chm_ge_cgc)])), c("dodgerblue4", "white", "firebrick4")),
        heatmap_legend_param = list(title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), title_position = "topcenter", legend_height = unit(2, "in")),
        na_col = "black",
        cluster_rows = FALSE, cluster_columns = FALSE,
        row_names_gp = gpar(fontsize = 5), show_column_names = FALSE,
        column_title = "Gene Expression",
        column_title_gp = gpar(fontsize = 30, fontface = "bold"),
        column_order = chm_plot_cgc_cols,
        row_order = chm_plot_cgc_rows,
        width = 1)
chm_cn_plot_cgc <- Heatmap(chm_cn_cgc, name = "Copy Number",
        col = colorRamp2(c(min(chm_cn_cgc[!is.na(chm_cn_cgc)]), 0, max(chm_cn_cgc[!is.na(chm_cn_cgc)])), c("white", "thistle2", "mediumorchid4")),
        heatmap_legend_param = list(title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), title_position = "topcenter", legend_height = unit(2, "in")),
        na_col = "black",
        cluster_rows = FALSE, cluster_columns = FALSE,
        row_names_gp = gpar(fontsize = 5), show_column_names = FALSE,
        column_title = "Copy Number",
        column_title_gp = gpar(fontsize = 30, fontface = "bold"),
        column_order = chm_plot_cgc_cols,
        row_order = chm_plot_cgc_rows,
        width = 1)
chm_mut_plot_cgc <- Heatmap(chm_mut_cgc, name = "Mutation Status",
        na_col = "black",
        heatmap_legend_param = list(title_gp = gpar(fontsize = 20), labels_gp = gpar(fontsize = 15), at = c(0, 1), labels = c("Other", "Mutant"), title_position = "topcenter", grid_height = unit(0.3, "in")),
        col = c("cyan3", "darkorchid"),
        cluster_rows = FALSE, cluster_columns = FALSE,
        row_names_gp = gpar(fontsize = 5), show_column_names = FALSE,
        column_title = "Mutation Status",
        column_title_gp = gpar(fontsize = 30, fontface = "bold"),
        column_order = chm_plot_cgc_cols,
        row_order = chm_plot_cgc_rows,
        width = 1)

chm_all_plot_cgc <- chm_plot_cgc + chm_ge_plot_cgc + chm_cn_plot_cgc + chm_mut_plot_cgc

pdf("./plots_18Q3/crispr_heatmap_pearson_all_cgc.pdf", width = 50, height = 50, paper = "special")
draw(chm_all_plot_cgc, heatmap_legend_side = "bottom", annotation_legend_side = "bottom")
seekViewport("annotation_Cell_Line_SSMD")
grid.text("Cell Line SSMD", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Cas9_Activity")
grid.text("Cas9 Activity", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Culture_Type")
grid.text("Culture Type", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Primary_Tissue")
grid.text("Primary Tissue", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Primary_Disease")
grid.text("Primary Disease", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Gender")
grid.text("Gender", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
seekViewport("annotation_Source")
grid.text("Source", unit(1, "npc") + unit(1, "mm"), 0.5, gp = gpar(fontsize = 15), default.units = "npc", just = "left")
dev.off()

8 Per-gene plots


8.1 Grouped plots

Plot code:

crispr_data_cgc <- filter(crispr_data, Hugo_Symbol %in% cgc$Hugo_Symbol)
crispr_all_genes <- unique(crispr_data_cgc$Hugo_Symbol)
crispr_all_groups <- lapply(crispr_all_genes, makeCRISPRgrob)
crispr_all_groups_paths <- paste0(crispr_all_genes, "_crispr_grouped.png")
pwalk(list(crispr_all_groups_paths, crispr_all_groups), ggsave, path = "./plots_18Q3/crispr_grouped_plots_cgc", dpi = 300, width = 12, height = 12, units = "in")

Data management:

crispr_all_bygene <- paste0(list.files("./plots_18Q3/crispr_grouped_plots_cgc", full.names = TRUE))
names(crispr_all_bygene) <- str_replace_all(crispr_all_bygene, c("_crispr_grouped.png" = "", "./plots_18Q3/crispr_grouped_plots_cgc/" = ""))
crispr_bygene_order <- c(intersect(crispr_signif$Hugo_Symbol, names(crispr_all_bygene)), setdiff(names(crispr_all_bygene), crispr_signif$Hugo_Symbol))
crispr_all_bygene <- crispr_all_bygene[match(crispr_bygene_order, names(crispr_all_bygene))]
bsselect(crispr_all_bygene, type = "img", selected = "KRAS", live_search = TRUE, show_tick = TRUE, height = 300, frame_height = 275)

8.2 Sorted lineage plots

Plot code:

crispr_data_cgc <- filter(crispr_data, Hugo_Symbol %in% cgc$Hugo_Symbol)
crispr_all_genes <- unique(crispr_data_cgc$Hugo_Symbol)
crispr_all_lins <- lapply(crispr_all_genes, makeCRISPRlinplot)
crispr_all_lins_paths <- paste0(crispr_all_genes, "_crispr_lineage.png")
pwalk(list(crispr_all_lins_paths, crispr_all_lins), ggsave, path = "./plots_18Q3/crispr_lineage_plots_cgc", dpi = 300, width = 12, height = 12, units = "in")

Data management:

crispr_all_bygene <- paste0(list.files("./plots_18Q3/crispr_lineage_plots_cgc", full.names = TRUE))
names(crispr_all_bygene) <- str_replace_all(crispr_all_bygene, c("_crispr_lineage.png" = "", "./plots_18Q3/crispr_lineage_plots_cgc/" = ""))
crispr_bygene_order <- c(intersect(crispr_signif$Hugo_Symbol, names(crispr_all_bygene)), setdiff(names(crispr_all_bygene), crispr_signif$Hugo_Symbol))
crispr_all_bygene <- crispr_all_bygene[match(crispr_bygene_order, names(crispr_all_bygene))]
bsselect(crispr_all_bygene, type = "img", selected = "KRAS", live_search = TRUE, show_tick = TRUE, height = 300, frame_height = 275)

8.3 Sorted tissue plots

Plot code:

crispr_data_cgc <- filter(crispr_data, Hugo_Symbol %in% cgc$Hugo_Symbol)
crispr_all_genes <- unique(crispr_data_cgc$Hugo_Symbol)
crispr_all_lins <- lapply(crispr_all_genes, makeCRISPRtissueplot)
crispr_all_lins_paths <- paste0(crispr_all_genes, "_crispr_tissue.png")
pwalk(list(crispr_all_lins_paths, crispr_all_lins), ggsave, path = "./plots_18Q3/crispr_tissue_plots_cgc", dpi = 300, width = 12, height = 12, units = "in")

Data management:

crispr_all_bygene <- paste0(list.files("./plots_18Q3/crispr_tissue_plots_cgc", full.names = TRUE))
names(crispr_all_bygene) <- str_replace_all(crispr_all_bygene, c("_crispr_tissue.png" = "", "./plots_18Q3/crispr_tissue_plots_cgc/" = ""))
crispr_bygene_order <- c(intersect(crispr_signif$Hugo_Symbol, names(crispr_all_bygene)), setdiff(names(crispr_all_bygene), crispr_signif$Hugo_Symbol))
crispr_all_bygene <- crispr_all_bygene[match(crispr_bygene_order, names(crispr_all_bygene))]
bsselect(crispr_all_bygene, type = "img", selected = "KRAS", live_search = TRUE, show_tick = TRUE, height = 300, frame_height = 275)

9 Report #2


9.1 Metadata

tissue_summ <- crispr_meta %>% group_by(primary_tissue) %>% tally()
disease_summ <- ccl_info %>% group_by(Primary.Disease) %>% tally()
ggplot(data = tissue_summ) + 
  aes(x = 0, y = n, fill = primary_tissue, label = primary_tissue) +
  geom_histogram(color = "black", stat = "identity") +
  geom_text(position = position_stack(vjust = 0.5)) +
  scale_y_continuous(breaks = seq(0, 500, by = 25), labels = seq(0, 500, by = 25)) +
  coord_cartesian(ylim = c(0, 500)) +
  theme(legend.position = "none", axis.text.x = element_blank(), axis.ticks.x = element_blank(), axis.title.x = element_blank()) +
  labs(y = "Count")

9.2 Scores

Distribution of scores across cell lines and primary tissues. Each data point represents a single cell line.

ceres_summ <- crispr_data %>% group_by(primary_tissue) %>% summarize(Max = max(Score),
                                                                     Min = min(Score))
score_summ_plot <- ggplot(data = crispr_data, aes(x = primary_tissue, color = primary_tissue)) +
  geom_boxplot(mapping = aes(y = Score), outlier.shape = NA) +
  geom_point(data = ceres_summ, mapping = aes(x = primary_tissue, y = Min)) +
  geom_point(data = ceres_summ, mapping = aes(x = primary_tissue, y = Max)) +
  scale_y_continuous(breaks = seq(-4, 6, by = 1), labels = seq(-4, 6, by = 1)) +
  theme(axis.text.x = element_text(angle = 30, hjust = 1), legend.position = "none") +
  labs(x = "Primary Tissue", y = "CERES Score")
score_summ_plot

# ggsave(filename = "./plots_18Q3/crispr/ceres_score_summ_plot.pdf", plot = score_summ_plot, width = 12, height = 4, device = "pdf")

9.3 Mutations

maf_df_filt <- filter(maf_df, Hugo_Symbol %in% unique(crispr_data$Hugo_Symbol))
# maf_summ_filt <- maf_df_filt %>% group_by(Mutation_Status) %>% tally()
# maf_summ_filt$Percent <- format(round(maf_summ_filt$n / sum(maf_summ_filt$n) * 100, 4), nsmall = 2)
# maf_summ_filt$Percent <- as.numeric(as.character(maf_summ_filt$Percent))

Distribution of mutations across cell lines and primary tissues. Each data point represents a single cell line.

maf_ccl_annot <- merge(maf_df %>% group_by(Broad_ID) %>% tally(), crispr_meta, by = "Broad_ID", all.y = TRUE)

maf_ccl_annot_plot <- ggplot(data = maf_ccl_annot, aes(x = primary_tissue, y = n, color = primary_tissue)) +
  geom_boxplot(outlier.shape = NA, fill = NA) +
  geom_jitter(alpha = 0.7, size = 1, position = position_jitter(w = 0.25)) +
  scale_y_continuous(breaks = seq(0, 9000, by = 1000), labels = seq(0, 9000, by = 1000)) +
  theme(axis.ticks.x = element_blank(), axis.title.x = element_blank(), axis.text.x = element_blank(), legend.position = "none") +
  labs(x = "Primary Tissue", y = "Number of Mutations")
maf_ccl_annot_plot

# ggsave(filename = "./plots_18Q3/crispr/maf_ccl_annot_plot.pdf", plot = maf_ccl_annot_plot, width = 12, height = 4, device = "pdf")
# ggsave(filename = "./plots_18Q3/crispr/score_mut_summ.pdf", plot = grid.draw(rbind(ggplotGrob(maf_ccl_annot_plot), ggplotGrob(score_summ_plot), size = "first")), width = 12, height = 8, device = "pdf")
maf_gene_annot <- maf_df_filt %>% group_by(Hugo_Symbol) %>% tally()

ggplot(data = maf_gene_annot, aes(x = n)) +
  geom_histogram(breaks = seq(0, 600, by = 20), fill = "darkslategray3", color = "black", alpha = 0.7) +
  coord_cartesian(ylim = c(0, 13000)) +
  scale_y_continuous(breaks = seq(0, 13500, by = 500), labels = formatC(seq(0, 13500, by = 500), format = "d")) +
  scale_x_continuous(breaks = seq(0, 600, by = 20), labels = seq(0, 600, by = 20)) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  stat_bin(breaks = seq(0, 600, by = 20), geom = "text", colour = "black", size = 2.5, aes(label = ..count..), vjust = -0.2, angle = 45, hjust = -0.1) +
  labs(x = "Number of Mutations", y = "Frequency", title = paste0("Distribution of mutational load for ", length(unique(maf_gene_annot$Hugo_Symbol)), " genes in the CCLE MAF file across all ", length(unique(crispr_ccl$Broad_ID)), " cell lines in the CRISPR screen"))

9.4 Dependency probabilities

drop_prob_100 <- ggplot(data = crispr_data, aes(x = Dep_Prob)) +
  geom_histogram(breaks = seq(0, 1, by = 0.05), fill = "darkslategray3", color = "black", alpha = 0.7) +
  scale_x_continuous(breaks = seq(0, 1, by = 0.05), labels = seq(0, 1, by = 0.05)) +
  scale_y_continuous(breaks = seq(0, 5500000, by = 500000), labels = formatC(seq(0, 5500000, by = 500000), format = "d")) +
  stat_bin(breaks = seq(0, 1, by = 0.05), geom = "text", colour = "black", size = 3.5, aes(label = ..count..), vjust = -1) +
  labs(title = "Distribution of all dependency probabilities", x = "Probability of Real Dependency", y = "Frequency")

dep_prob_5 <- ggplot(data = filter(crispr_data, Dep_Prob >= 0.95), aes(x = Dep_Prob)) +
  geom_histogram(mapping = aes(fill = primary_tissue), breaks = seq(0.95, 1, by = 0.005), color = "black") +
  scale_y_continuous(breaks = seq(0, 60000, by = 10000), labels = formatC(seq(0, 60000, by = 10000), format = "d")) +
  theme(legend.position = "bottom") +
  guides(fill = guide_legend(nrow = 4, byrow = FALSE)) +
  stat_bin(breaks = seq(0.95, 1, by = 0.005), geom = "text", color = "black", size = 3.5, aes(label = ..count..), vjust = -1) +
  labs(title = "Distribution of all dependency probabilities > 0.95", x = "Probability of Real Dependency", y = "Frequency", fill = "Primary Tissue")

9.5 Per-gene plots

sig_genes <- c("KRAS", "TP53", "NRAS", "BRAF", "PIK3CA", "PTEN", "CTNNB1")
crispr_sig_genes <- filter(crispr_data, Hugo_Symbol %in% sig_genes)
crispr_sig_genes$Hugo_Symbol <- factor(crispr_sig_genes$Hugo_Symbol, levels = c("KRAS", "TP53", "NRAS", "BRAF", "PIK3CA", "PTEN", "CTNNB1"))

crispr_color <- as.character(crispr_sig_genes$Color)
names(crispr_color) <- crispr_sig_genes$Mutation_Status

# Mutation status
plot_mut <- ggplot(data = crispr_sig_genes, aes(x = Mutation_Status, y = Score, color = Mutation_Status)) +
  geom_boxplot(outlier.shape = NA) +
  geom_jitter(alpha = 0.3, size = 0.7, position = position_jitter(w = 0.05)) +
  facet_wrap(~ Hugo_Symbol, nrow = 7, scales = "free_y") +
  scale_color_manual(values = crispr_color) +
  geom_hline(yintercept = 0, linetype = 2, lwd = 0.3) +
  theme_light() +
  theme(legend.position = "none") +
  labs(x = "Mutation Status", y = "CERES Score",
       title = "Mutation Status")
# plot_mut

# Copy number
plot_cn <- ggplot(data = crispr_sig_genes, aes(x = Copy_Number, y = Score, color = Mutation_Status)) +
  geom_point(size = 0.5, alpha = 0.5) +
  geom_smooth(method = "lm", size = 0.5) +
  scale_color_manual(values = crispr_color) +
  facet_wrap(~ Hugo_Symbol, nrow = 7, scales = "free") +
  stat_cor(method = "pearson", show.legend = FALSE, label.x.npc = "right", label.y.npc = "top") +
  theme_light() +
  theme(legend.position = "none", axis.title.y = element_blank()) +
  labs(y = "CERES Score", color = "Mutation Status",
       x = "Theoretical Copy number", title = "Copy Number")
# plot_cn

# Gene expression
plot_ge <- ggplot(data = crispr_sig_genes, aes(x = RPKM_log2, y = Score, color = Mutation_Status)) +
  geom_point(size = 0.5, alpha = 0.5) +
  geom_smooth(method = "lm", size = 0.5) +
  facet_wrap(~ Hugo_Symbol, nrow = 7, scales = "free_y") +
  scale_color_manual(values = crispr_color) +
  stat_cor(method = "pearson", show.legend = FALSE, label.x.npc = "left", label.y.npc = "top") +
  theme_light() +
  theme(legend.position = "none", axis.title.y = element_blank()) +
  labs(y = "CERES Score", color = "Mutation Status",
       x = "Gene Expression [log2(RPKM)]",
       title = "Gene Expression")
# plot_ge

# ggsave(filename = "./plots_18Q3/crispr/omics_sig_genes.pdf", plot = grid.draw(cbind(ggplotGrob(plot_mut), ggplotGrob(plot_ge), ggplotGrob(plot_cn), size = "last")), width = 15, height = 18, device = "pdf")

10 shRNA


10.1 Data and annotations

DepMap cell line metadata:

# From figshare
shrna_meta <- read.delim("./data_munging/sample_info_18Q3_shrna.csv", sep = ",", header = TRUE, na.strings = c("", NA))
colnames(shrna_meta)[1] <- "CCLE_Name"

The Achilles shRNA DEMETER score data was pulled from the CTD2 Data Portal (Tsherniak et al 2017).

shrna <- read.table("./data_munging/D2_combined_gene_dep_scores.csv.gz", sep = ",", header = TRUE, check.names = FALSE)
colnames(shrna)[1] <- "Hugo_Symbol"
# Remove Entrez gene IDs from gene names
shrna$Hugo_Symbol <- gsub(" .*", "", shrna$Hugo_Symbol)

# Melt shRNA dataset for merging
shrna_melt <- melt(shrna , id.vars = "Hugo_Symbol", measure.vars = colnames(shrna)[2:ncol(shrna)], variable.name = "CCLE_Name", value.name = "Score")
shrna_melt <- drop_na(shrna_melt)

Merge annotation data:

# Merge cell line metadata
shrna_melt <- merge(shrna_melt, ccl_info, by = "CCLE_Name", all.x = TRUE)
shrna_melt <- merge(shrna_melt, shrna_meta, by = "CCLE_Name", all.x = TRUE)

# Merge mutation annotations
shrna_muts <- merge(shrna_melt, maf_df, by = c("Hugo_Symbol", "CCLE_Name", "Broad_ID"), all.x = TRUE)
shrna_muts <- shrna_muts %>% mutate(Mutation_Status = if_else(is.na(Mutation_Status), "Other", Mutation_Status))
shrna_muts$Hugo_Symbol <- factor(shrna_muts$Hugo_Symbol)

# Summarize number of mutant and Other cell lines
shrna_muts_summ <- shrna_muts %>% group_by(Hugo_Symbol) %>%
  summarize(N_Other = sum(Mutation_Status == "Other"),
            N_Mutant = sum(Mutation_Status == "Mutant"))

# Merge test results back into full dataset, which restores information lost in the summarization
shrna_data <- merge(shrna_muts_summ, shrna_muts, by = "Hugo_Symbol")

# Add Color column
shrna_data$Color <- ifelse(shrna_data$Mutation_Status == "Other", "cyan3", "darkorchid")
shrna_data$Color <- factor(shrna_data$Color)

# Cell line lineages
shrna_data <- merge(shrna_data, ccl_converter, by = c("CCLE_Name", "Broad_ID"), all.x = TRUE)
levels(shrna_data$lineage_name) <- sort(levels(shrna_data$lineage_name), decreasing = TRUE)

# Copy number
shrna_data <- merge(shrna_data, cn_melt, by = c("Hugo_Symbol", "Broad_ID"), all.x = TRUE)

# Gene expression (RPKM)
ge_filt <- filter(ge_melt, Hugo_Symbol %in% unique(shrna_data$Hugo_Symbol))
shrna_data <- merge(shrna_data, ge_filt, by = c("Hugo_Symbol", "Broad_ID", "CCLE_Name"), all.x = TRUE)

saveRDS(shrna_data, "./../crispr_lineages_giant_files/shrna_data_18Q3.rds", compress = "xz")
shrna_data <- readRDS("./../crispr_lineages_giant_files/shrna_data_18Q3.rds")
shrna_ccl <- data.frame("Broad_ID" = shrna_data$Broad_ID)

10.2 Wilcoxon tests

10.2.1 Grouped by gene

shrna_signif <- compare_means(Score ~ Mutation_Status, group.by = c("Hugo_Symbol"), data = shrna_data, method = "wilcox.test", p.adjust.method = "BH")
shrna_signif <- adj_signif(shrna_signif)
shrna_signif <- shrna_signif[order(shrna_signif$p),]
saveRDS(shrna_signif, "./data_munging/rds/shrna_signif.rds")
shrna_signif <- readRDS("./data_munging/rds/shrna_signif.rds")

knitr::kable(filter(shrna_signif, p < 0.01)[, c("Hugo_Symbol", "p", "p.adj", "p.format", "p.signif", "p.signif.adj")], caption = "shRNA Screen: Wilcoxon Test Results Comparing Mutant and Other Cell Lines, p < 0.1  (Benjamini-Hochberg-corrected p-values: * p <= 0.05, ** p <= 0.01, *** p <= 0.001, **** p <= 0.0001)") %>% kable_styling(bootstrap_options = c("striped", "hover", "condensed", "responsive")) %>% scroll_box(width = "900px", height = "450px")

10.2.2 Grouped by gene and lineage

shrna_signif_lineage <- compare_means(Score ~ Mutation_Status, group.by = c("Hugo_Symbol", "lineage_name"), data = shrna_data, method = "wilcox.test", p.adjust.method = "BH")
shrna_signif_lineage <- adj_signif(shrna_signif_lineage)
shrna_signif_lineage <- mutate(shrna_signif_lineage, lineage_name = reorder(lineage_name, p.adj, mean))
saveRDS(shrna_signif_lineage, "./../crispr_lineages_giant_files/shrna_signif_lineage.rds")
shrna_signif_lineage <- readRDS("./../crispr_lineages_giant_files/shrna_signif_lineage.rds")

11 References


Barretina, J., Caponigro, G., Stransky, N., Venkatesan, K., Margolin, A. A., Kim, S., … Garraway, L. A. (2012). The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature, 483(7391), 603–607. https://doi.org/10.1038/nature11003

Broad Institute Cancer Dependency Map; Cancer Data Science (2018): Cancer Dependency Map, CRISPR Avana dataset 18Q3 (Avana_public_18Q3). figshare. Fileset. doi:10.6084/m9.figshare.6931364.v1

Consortium, T. C. C. L. E., & Consortium, T. G. of D. S. in C. (2015). Pharmacogenomic agreement between two cancer cell line data sets. Nature, 528(7580), 84–87. https://doi.org/10.1038/nature15736

Data Science, Cancer (2018): DEMETER2 data. figshare. Fileset. doi:10.6084/m9.figshare.6025238.v2

Doench, J. G., Fusi, N., Sullender, M., Hegde, M., Vaimberg, E. W., Donovan, K. F., … Root, D. E. (2016). Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9. Nature Biotechnology, 34(2), 184–191. https://doi.org/10.1038/nbt.3437

Meyers, R. M., Bryan, J. G., McFarland, J. M., Weir, B. A., Sizemore, A. E., Xu, H., … Tsherniak, A. (2017). Computational correction of copy-number effect improves specificity of CRISPR-Cas9 essentiality screens in cancer cells. Nature Genetics, 49(12), 1779–1784. https://doi.org/10.1038/ng.3984

McFarland, J. M., Ho, Z. V., Kugener, G., Dempster, J. M., Montgomery, P. G., Bryan, J. G., … Tsherniak, A. (2018). Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration. https://doi.org/10.1101/305656

12 Session information


print(sessionInfo())
## R version 3.5.0 (2018-04-23)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS High Sierra 10.13.6
## 
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] grid      parallel  stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] bindrcpp_0.2.2        circlize_0.4.4        ComplexHeatmap_1.18.1
##  [4] bsselectR_0.1.0       caret_6.0-80          lattice_0.20-35      
##  [7] reshape2_1.4.3        devtools_1.13.6       glmnet_2.0-16        
## [10] foreach_1.4.4         Matrix_1.2-14         broom_0.5.0          
## [13] gridExtra_2.3         kableExtra_0.9.0      matrixStats_0.54.0   
## [16] forcats_0.3.0         stringr_1.3.1         dplyr_0.7.6          
## [19] purrr_0.2.5           readr_1.1.1           tidyr_0.8.1          
## [22] tibble_1.4.2          tidyverse_1.2.1       plyr_1.8.4           
## [25] CePa_0.6              data.table_1.11.4     rowr_1.1.3           
## [28] ggsignif_0.4.0        ggpubr_0.1.7.999      magrittr_1.5         
## [31] ggplot2_3.0.0         NMF_0.21.0            Biobase_2.40.0       
## [34] BiocGenerics_0.26.0   cluster_2.0.7-1       rngtools_1.3.1       
## [37] pkgmaker_0.27         registry_0.5         
## 
## loaded via a namespace (and not attached):
##  [1] colorspace_1.3-2    rjson_0.2.20        class_7.3-14       
##  [4] rprojroot_1.3-2     GlobalOptions_0.1.0 pls_2.6-0          
##  [7] rstudioapi_0.7      DRR_0.0.3           prodlim_2018.04.18 
## [10] lubridate_1.7.4     xml2_1.2.0          splines_3.5.0      
## [13] codetools_0.2-15    doParallel_1.0.11   robustbase_0.93-1.1
## [16] knitr_1.20          RcppRoll_0.3.0      jsonlite_1.5       
## [19] gridBase_0.4-7      ddalpha_1.3.4       kernlab_0.9-26     
## [22] sfsmisc_1.1-2       graph_1.58.0        compiler_3.5.0     
## [25] httr_1.3.1          backports_1.1.2     assertthat_0.2.0   
## [28] lazyeval_0.2.1      cli_1.0.0           htmltools_0.3.6    
## [31] tools_3.5.0         igraph_1.2.1        gtable_0.2.0       
## [34] glue_1.3.0          Rcpp_0.12.18        cellranger_1.1.0   
## [37] nlme_3.1-137        iterators_1.0.10    timeDate_3043.102  
## [40] gower_0.1.2         rvest_0.3.2         DEoptimR_1.0-8     
## [43] MASS_7.3-50         scales_0.5.0        ipred_0.9-6        
## [46] hms_0.4.2           RColorBrewer_1.1-2  yaml_2.1.19        
## [49] memoise_1.1.0       rpart_4.1-13        stringi_1.2.4      
## [52] highr_0.7           bibtex_0.4.2        shape_1.4.4        
## [55] lava_1.6.2          geometry_0.3-6      rlang_0.2.1        
## [58] pkgconfig_2.0.1     evaluate_0.11       bindr_0.1.1        
## [61] labeling_0.3        htmlwidgets_1.2     recipes_0.1.3      
## [64] CVST_0.2-2          tidyselect_0.2.4    R6_2.2.2           
## [67] dimRed_0.1.0        pillar_1.3.0        haven_1.1.2        
## [70] withr_2.1.2         nnet_7.3-12         survival_2.42-6    
## [73] abind_1.4-5         modelr_0.1.2        crayon_1.3.4       
## [76] rmarkdown_1.10      GetoptLong_0.1.7    readxl_1.1.0       
## [79] Rgraphviz_2.24.0    ModelMetrics_1.1.0  digest_0.6.15      
## [82] xtable_1.8-2        stats4_3.5.0        munsell_0.5.0      
## [85] viridisLite_0.3.0   magic_1.5-8